Extensive assessment and evaluation methodologies on ... - umexpert

Information Sciences 301 (2015) 305–344

Contents lists available at ScienceDirect

Information Sciences journal homepage: www.elsevier.com/locate/ins

Extensive assessment and evaluation methodologies on assistive social robots for modelling human–robot interaction – A review Doreen Ying Ying Sim a,⇑, Chu Kiong Loo b a b

Faculty of Cognitive Science and Human Development, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia Faculty of Computer Science and Information Techonology, University of Malaya, Wilayah Persekutuan, Kuala Lumpur, Malaysia

a r t i c l e

i n f o

Article history: Received 15 August 2013 Received in revised form 28 November 2014 Accepted 4 December 2014 Available online 24 December 2014 Keywords: Extensive evaluation Assessment Human-Robot Interaction (HRI) Assistive social robot A better modelling approach New vision

a b s t r a c t Assessment and evaluation methodologies as well as combinations of them, for modelling of Human–Robot Interaction (HRI), are reviewed extensively and thoroughly in this paper. However, based on the types of robots and the kinds of interactions involved in the modelling of HRI, we concentrate just on the assistive social robot types. A comprehensive review has been done on each of these extensive evaluation and assessment methodologies applied for testing the usability of assistive social robots, user acceptance towards robots and robot acceptance in terms of behavioural adaptation during the HRI. The evaluation methodologies are reviewed based on the primary and non-primary basis, while the assessment methodologies are reviewed based on the type(s) of modelling approaches. We then discussed the weaknesses, strengths and uniqueness of each type of the past research work done on the evaluation and assessment methodologies. Comparison and contrast tables are also illustrated. Lastly, this paper provides our recommended directions, new vision, as well as our inspirations and new insights for future researches by highlighting the key areas for enhancing each of the past evaluation and assessment methodologies so that a better modelling approach for HRI can be achieved. Contributions of this review paper are also discussed thoroughly. Ó 2014 Elsevier Inc. All rights reserved.

1. Introduction Assessing acceptance in robots needs a methodology or a series of methodologies that is often used to measure the willingness of people to use a technology. This needs a type of modelling, which is always known as the Technology Acceptance Modelling (TAM) [20,21,23,55,57,61,63,135]. Nowadays, significant increase in the elderly population and the increased shortage of labour, as well as the explosion of costs in our daily expenses, have posed extreme challenges to our society [55,61,63,152]. However, how many research projects can explore the applicability of technological advances such as Intelligent Systems that enable people to live independently? In this paper, we discussed our notion of the concepts ‘social’ and ‘assistive’ within the context of robots used by people nowadays. So, the two main robot types that we review are of ‘social’ and ‘assistive’ (see Fig. 1). Then, we deepen our understanding of these concepts and review the examples of the developments of these two main robot types, mainly involved in the long-term modelling of Human–Robot Interaction ⇑ Corresponding author. Tel.: +60 82 244942; fax: +60 82 332641. E-mail address: [email protected] (D.Y.Y. Sim). http://dx.doi.org/10.1016/j.ins.2014.12.017 0020-0255/Ó 2014 Elsevier Inc. All rights reserved.

306

D.Y.Y. Sim, C.K. Loo / Information Sciences 301 (2015) 305–344

Fig. 1. A general categorization of robots (Heerink [55]). This paper mainly focuses on Assistive Social Robots category, under which there are Service Robots and Companion Robots.

(HRI), from past researches. Ultimately, we try to categorize their respective evaluation and assessment methodologies for HRI. We thereafter compare and contrast each of these evaluation and assessment methodologies for long-term modelling of HRI, mainly in terms of their characteristics, strengths and weaknesses. Human–Robot Interaction (HRI) is a rapidly advancing area of research, and as such there is a growing need for strong and efficient methods of assessment and evaluation. This brings credibility and validity to the scientific research. According to Kidd and Breazeal in 2005, and some other researchers, two primary issues observed in the HRI studies are the lack of significantly sized participant pools that closely represent the populations being studied and the lack of multiple methods of assessment used to obtain convergent validity in HRI studies [5,92]. Social robots are robots that people apply a social model in order to interact with them more efficiently, understand them deeper and build up more intimate relationships [22,31,37,40,42]. When we focus on the social needs, some studies have demonstrated how robots can have the ability to provide a kind of ‘pet-like’ companionship [30,40,174–177] (see Fig. 1). AIBO was the first interactive robot to prove successful in the commercial market [55,152], and since it behaves like a real pet [42,152]. Things that are more interesting is that Kanda et al., in 2009, found that a series of abstraction techniques for people’s trajectories and a service framework for using these techniques in a social robot, can enable a designer to make the social robots proactively approach customers. This can be done by only providing information about target local behaviour to the social robots [79]. In an even more recent research done in 2011, Moriguchi et al. have shown that young children can learn new actions and skills from a non-human agent, such as a robot [111]. All these researches demonstrated how robots can anticipate the need for a social entity by the users to build a good emotional relationship [40,55]. However, as shown by researches done by Bickmore in 2004, as well as Wu and Miller, together with some other researchers in 2005, the possibility to build an emotional relationship not only responds to social needs, but also increases acceptability by the society. This emotional relationship is combined with the ease of use of an interface that is controlled by social interaction [9,11,179,180]. In addition, certain researches done by Kanda et al. in 2004, 2007 and 2009 have shown how recent progress in robotics could affect children’s lives [80,88,89]. They showed how the robot had significantly affected children’s behaviours, feelings, and even their friendships. Their studies had provided clues to the process of children’s adaptation to interactions with robots, and particularly on how they started to treat robots as intelligent beings [88]. All these past researches have shown that robots have significantly affected us, in all ages [121]. In a similar vein, we also review all the recent related work for the evaluation and assessment methodologies for HRI. Before any interface related to robotics can be evaluated, it is necessary to understand the users’ relevant skills and mental models in order to develop evaluation criteria with those users in mind [151,182]. In the past, evaluations based on empirically validated sets of heuristics have been used on the desktop user interfaces and web-based applications [120]. However, recent human–robot interfaces differ very widely depending on the platforms and sensors. In addition, existing guidelines are not adequate to support the heuristics evaluation [120,182]. Hence, in this paper, we need to review both the evaluation and assessment methodologies for HRI. From the past researches, in the year 2002, Scholtz proposed six evaluation guidelines that can be used as high-level evaluation criteria for Human–Computer Interaction (HCI) or Human–Robot Interaction (HRI) [144,182]. Now, we wish to see different types of assessment and evaluation methodologies for HRI researched and proposed by the past researchers so far. 2. Differences between the assessment and evaluation methodologies for HRI In this paper, we categorize all the methodologies with characterization based on the differences between the assessment and evaluation methodologies applied by the researchers for modelling HRI.


307

What are the differences between Assessment and Evaluation Methodologies? We try to categorize the major characteristics of them as below in order to review what have been done by the past researchers in using each of them toward Human–Robot Interaction, or HRI. The major characteristics of each of them are listed as below: Major characteristics of the Assessment Methodologies for assessing HRI include: (1) (2) (3) (4)

Process-oriented. Diagnostic approach as it identifies areas for continuous improvement and long-term modelling for the HRI. Ongoing as it is formative to improve the learning approaches to promote HRI from time to time. Continuous as this methodology takes quite some time, e.g. learning approaches, monitoring control, etc.

Major characteristics of the Evaluation Methodologies for evaluating HRI include: (1) (2) (3) (4)

Product-oriented. Judgmental approach as it arrives at a concluding judgmental score, rating or grade for the HRI. Final as it is summative to sum up the evaluation scores or ratings for the HRI. Discrete as this methodology does not need significant amount of time, e.g. experimental results, scores from users’ feedbacks on questionnaires, evaluation scores from participants and the like.

Evaluation methodologies on HRI are reviewed based on the primary and non-primary basis. The primary evaluation methodologies are those that directly evaluate the HRI, while the non-primary ones are indirectly evaluating the HRI (refer to Tables 1 and 2 for detailed illustrations). Non-primary evaluation is an indirect evaluation on HRI based on other parameters such as numerical analysis on body movements, Ease of Classification (EOC), and non-verbal behaviours of the robot (see Table 2 for more details). Evaluation methodologies from the Human–Computer Interaction (HCI) and ComputerSupported Cooperative Working (CSCW) can be adapted for the use in HRI provided they take into account the complex, dynamic, and autonomous nature of robots [155,182]. Assessment Methodologies applied on HRI are mainly based on the types of models and modelling approaches. Assessment Methodologies on (I) humanoid robots, and (II) non-humanoid robots, such as embodied robots, are profoundly quite different [65,68,98,106,112]. We review these different assessment methodologies respectively to human-and-humanoid robot interaction as well as human-and-non-humanoid robot interaction. Different types of models and modelling approaches are reviewed to see how researchers have applied them in order to assess and long-term model the HRI. 3. Types of modelling and measurement tactics involved in the assessment methodologies for HRI Before we review specifically each of the Assessment and Evaluation Methodologies, we review the modelling and measurement tactics involved in assessing and long-term modelling of HRI, i.e. Assessment Methodologies. 3.1. Social models mainly involved in assessing the emulation of empathy during HRI Taking a look at the studies done by Burke et al. in 2004, especially in their research insights into HRI in the larger context of urban search and rescue, the information needs for the operator in HRI fell into several categories. These include (I) information about the status of the robot, (II) information about the robot’s environment [15], and (III) information about victims found in the environment [13]. So, how did they assess the HRI in terms of the information given to the operators who operated the robots? The information about the status of the robot and the robot’s environment is necessary for real-time monitoring and control or supervision of the search. The operator involved in the HRI uses information about the victim state and location to ensure coverage [13,182]. Then, the past research on behavioural control architecture, presented in the research done by Tapus and Mataric’ [167], take into account on different factors. These factors include (I) proxemics, (II) verbal and non-verbal communication, as well as (III) robot activity [166,168]. According to the researches done by them, two of those elements are also very useful in emulating empathy: verbal and non-verbal communication. Proximity, or the interpersonal distance, is another important element they have been explored because it plays a key role in human interactions. As found out from their research, it is well known that people have stronger empathic emotions and reactions when the interaction episodes are associated with others with whom they have a social relationship (such as friends and family) or a common background (such as a person who lived through a similar experience) [166]. The above researches stressed that, in order to be able to use the factor discussed above, humans need to create strong bonds with robots for the nature similar to those formed with other humans. They found out that understanding human affect and reacting appropriately to different social situations, such as to avoid misunderstandings but to permit natural human–robot interaction, will lead toward an improved empathic appearance of the robot. As stated by them, verbal and non-verbal communication provide social cues that make robots appear more intuitive and natural. According to them, they are two ways of mediating empathy, i.e. (1) cognitive, and (2) affective. In terms of cognitive empathy, the robot should show empathy as if it understands others’ emotions; emotions, robot can behave as if the others’ emotions affect it. In terms of

308

Table 1 Strengths, weaknesses and uniqueness of the primary evaluation methodologies for long-term modelling of hri. Author(s) and Year(s) (key authors only) – (in Chronological Order)

Strengths of this Primary Evaluation Methodology Primary Evaluation Methodology on HRI (references are just the key representing ones)

Weaknesses of this Primary Evaluation Methodology

Favourable Assessment Model(s) or Modelling Approaches to incorporate with each Primary Evaluation Methodology as stated

Bethel et al. [8]

(1) Self-Assessments or Much easier and faster to measure than objective methods because mostly are just the questionnaires given for participants to fill for Self-Report subjective assessments. Some parts are just observations, and there Methodologies [5,8] is no specific professional measurement which needs prior knowledge or prior study to be taken place

(1) Problems with validity and corroboration because participants may answer questions unrealistically; (2) This is a subjective method as questionnaires are used. So, bias may incur; (3) Observers may not be able to directly corroborate the information provided by the participants. Hence, observation results may not be accurate

(1) Behavioural Adaptation Model;

Bethel and Murphy [5]

(2) Human Gaze Model or other gaze models;

This method can be very biased because (same as above – i.e. 1st row, last column, of this table) ‘Hawthorne effect’ is a well-known phenomenon in observation studies which can incur bias

Bethel et al. [8] Bethel and Murphy [5]

Less tedious if compared with the subjective measurements as (2) Behavioural measurements through mostly are observation jobs involved. Usually, observation works well with participants self-assessment responses observations [5,8]

Bethel et al. [7,8]

(3) Psychophysiological measurements [5,7,8]

(1) Very objective method and hence very much less biased; (2) a non-invasive method that determines the stress levels and reactions of participants interacting with the technology; (3) video observations are often recorded for visual and auditory information. So, a method of least biased

(1) It can complicate the process as the results may not be straightforward and confounds can lead to data misinterpretation;(2) Participants’ information needs to be obtained prior to the study; (3) The measurement process can be tedious and/or time-consuming; (4) Measurements on participants’ autonomic system responses may not be very accurate due to certain confounding factors

(1) Behavioural Adaptation Model; (2) User–Robot Personality Matching Model; (3) Empathic/Emotional/TAME/Therapeutic/ Psychological Model (4) Godspeed Key Concepts Modelling (5) UTAUT Model

(4) Task performance metrics [5,8,14,119] [122,126,158] (may incorporate the subevaluation methodologies of comparisons of the measurement of body movement interaction in between a humanoid robot and humans with subjective evaluation results [25] [25,26,39,82] [83])

(1) Very useful when HRI is involving more than one person or one robot – it is very good for team scoring since variables of interest can be pre-set in the selection criteria for task performance; (2) Much less biased approach too when comparisons may get involved in the assessment methodologies with the subjective evaluation results

(1) Not very suitable for one-to-one HRI, and (2) less flexible method if major subjective assessments are needed from the participants; (3) the metrics designed may not be thorough and/or specific enough to evaluate every kind of HRI; (4) When this method is too HRI oriented, it is not generalised enough in applying to every kind of HRI; (5) This method is not very suitable for robots which are not humanoid robots; (6) This method is not very suitable for HRI which involves mainly verbal behaviours where actions are not important; (7) Only wellcoordinated behaviours correlate with the subjective evaluation scores. So, limited applications to all body movements

(1) Temporal Awareness Model/Timing Model; (2) Human–Robot Team (HRT) Modelling or Tele-Operated Multiple Robot Model (3) Robot Awareness Model (4) Model of Integrated Humans’ shared Intentions, e.g. Haptic Channel, Motion Planning Model through Play Interactions, and the like

Bethel and Murphy [5]

Kanda et al. [83] Olsen and Goodrich [122] Burke et al. [14] Kanda et al. [80,82] Steinfeld et al. [158] Bethel et al. [8] Bethel and Murphy [5] Mutlu et al. [119] Cooney et al. [25,26] Frank et al. [39] Pateraki et al. [126]


(3) User-Personality Matching Model; (4) Empathic/Emotional/TAME/Therapeutic/ Psychological Model (5) Human Friendship Estimation Model (6) Godspeed Key Concepts Modelling (7) UTAUT Model

Table 2 the strengths, weaknesses and uniqueness of each of the non-primary evaluation methodologies. Non-Primary Evaluation Methodologies on evaluating HRI

Strengths of this Non-Primary Evaluation Weaknesses of this Non-Primary Methodology Evaluation Methodology

Uniqueness of the Characteristics of this Non-Primary Evaluation Methodology

Mutlu et al. [118] Gockley et al. [45] Riek and Robinson [131] Sung et al. [163] Mutlu et al. [117]

Ease of Classification (EOC) Formula Scoring method for evaluating the Societal Acceptance towards robot

(1) EOC is used as a handy basis for comparison among different user groups, physical interaction spaces, and the like; (2) quick and easy to indirectly evaluate HRI; (3) it is designed to be a flexible metric, as non-primary method, that can accommodate the needs of different user groups and different user types

(1) EOC method is designed to deal with users’ ‘‘first impressions’’ of a robot, their views may change over time; (2) it is unclear how straight-forward it will be to adjust the EOC score to fully accommodate different contexts, user bases and robot types. So, method is straight-forward but less specific

Ease of Classification (EOC) Formula Score is generally very applicable as a nonprimary evaluation methodology for evaluating most types of HRI because the evaluation scoring formula and method is very straight-forward and standardised

Hayashi et al. [52] Hayashi et al. [53] Hayashi et al. [51]

Using robot’s conversation as a passive social medium –(Robot Manzai is used as a passive social medium for indirect evaluation on HRI to be conducted.)

Development of a multi-robot cooperation system for HRI – an indirect evaluation by using humanoid robots as passive social media, serving like televisions or computers

Timing and technical adjustments between robots, as passive-scoial media, in the multi-robot cooperation system can sometimes be difficult

(1) Robot Manzai system shows the potentiality of robots as passive-social media (such as televisions etc.); (2) Robot acting as a passive-social medium is the most effective way of attracting people’s interest

Jacobsson et al. [74]; [73]

See-Puck – name of the platform, the open interactive robot platform, for exploring Human–Robot relationships

(1) Users can indirectly evaluate the HRI by influencing the visuals of Glow-Bots. The evaluation outcome is a slowly evolving, constantly collection of autonomous robotic display; (2) Open source all hardware and software so that anyone can revise, extend upon or improve the displays- this open robot platform encourgaes non-primary evaluation of HRI

(1) GlowBots are demonstrated only for a few days, so unable to show a truly longlasting relationship between robots and humans. It may be necessary to sustain interest over weeks and months; (2) As not using LEDs as sensors, could not make the LED display touch sensitive. So, users could not directly influence what is seen on the display – makes indirect evaluation on HRI less reliable or accurate

(1) See-Puck platform enables users to explore new roles of robots in everyday environments; (2) Platform is not limited to a particular application; (3) In the proof-of-concept application, humans and robots can engage in a playful open-ended interaction; (4) See-Puck sets more opportunities in robotics, and the like during HRI

Kanda et al. [83] Kanda et al. [82]

Non-primary evaluation aspect involves numerical analysis of body movements. It measures the body movement interactions between a humanoid robot and humans

(1) Widely applicable in embodied communication, and it is based on a psychological method; (2) Estimation of momentary evaluation makes robots more adaptive in interacting with humans; (3) Indirect evaluation can relate conversational expressiveness to social presence and acceptance towards a robot

(1) As using an optical motion-capturing system to measure body movements, high resolution in time and space is needed; (2) The system may create technical difficulties and inaccuracies during measurements; (3) Analyses of body movements may be tedious or timeconsuming

(1) Comparisons of body movements and entrainment score reveals importance of well-coordinated behaviours and performance of the developed interactive behaviours; (2) Estimation of momentary evaluation score is a good analytical approach for HRI

Kamasima et al. [76] Kanda et al. [80]; Kozima et al. [97] Kanda et al. [86] Kozima et al.[96] Mutlu et al. [119] Satake et al. [142] Shukla and Tripathi [154] Liu et al. [104] Sorbello et al. [156]

Non-verbal behaviours of the robot and/or the robot’s design(s) have appropriate predictabilities for the users to tune its behaviour through manipulation – this is consistent with Proximity Theory in Psychology. This can combine well with societal acceptance modelling for HRI

(1) Robot’s design and/or its non-verbal behaviour is effective to intuitively convey the robot’s expressions of attention and emotion; (2) As robot’s non-verbal behaviours may be affected by users’ impressions and other attributions, using Proximity Theory to indirectly evaluate HRI is appropriate

Non-verbal behaviours of the robot may take a long time to establish interpersonal coordination and interactional synchrony with the participants before the nonprimary evaluation of HRI can be conducted

(1) Synchronized rhythm in establishing engagement during HRI can be evaluated using non-primary evaluations on proximity; (2) Observed non-verbal behaviours suggest robot’s design is effective in motivating user to share mental states; (3) This evaluation methodology can be used to flourish the Societal Acceptance Modelling and Psychological with Therapeutic Modelling


Author(s)/Year (key authors only – in chronological order)

309

310


affective empathy, the robot should manifest emotions through its facial expressions, voice, body postures, movements, and gestures so as to fit the situational context [166]. Robot does not feel empathy in any real sense, but it is projecting empathy through the recognized means of expressions overviewed by Ekman’s research [33–35]. In 2013, i.e. last year, Leite et al. supported that robot’s empathy needs to be sufficiently believable, but not to the extreme end of being so realistic to provoke expectations that cannot be met in reality [101]. 3.2. Technology Acceptance Model (TAM) and various types of methodologies involved in user acceptance As shown in Fig. 1, assistive robots can be divided into social robots and non-social robots. Assistive social robots can also be further divided into two subcategories, i.e. service robots and companion robots [55]. The non-social robot concerns physical assistive technology that is usually developed for rehabilitation purposes, but that is not in any way socially interactive. On the other hand, the other type, i.e. social assistive robot, is of course very socially interactive. These social assistive robots are systems that can be perceived as social entities which communicate with the user, or are communicated with the user such as through touching and sensing. Between these two categories, of course there is an overlap. Since there are also projects on social robots that are to be used for rehabilitation purposes [55,169], but generally in robotics these are in fact separated fields [55]. For HRI, especially for robots and screen agents (see Fig. 4a), starting from applying heuristics evaluation [24], or other usability type tests [182], classification tests [131] and role-based evaluation methodologies [143] and eventually to physical response measurements [28,57]. Technology Acceptance Model (TAM) is used as a methodology that does not only provide insights in the probability of acceptance of a specific technology, but also in the influences underlying acceptance tendencies [57,135]. In 1983, Davis proposed the Inter Personality Index (IRI) to assess empathy [32] during interaction, but since our main focus is on the evaluation and assessment methodologies for human–robot interaction, this review paper mainly reviews all the critical evaluation and assessment methodologies which had been developed by the researchers to promote long-term modelling of HRI. Davis’ Inter Personality Index is just reviewed as one of the assessment methodologies used by the past researchers, such as by Gonsior et al. in 2011 [47], to incorporate as an assessment methodology to promote HRI. Venkatesh et al. [172] evaluated eight theoretical models that employ intention and/or usage as the key dependent variable, i.e. (i) Theory of Reasoned Action, (ii) Motivation Model, (iii) Theory of Planned Behaviour (TPB), (iv) TAM, (v) a combined TAM and TPB Model, (vi) Model of Perceived Control (PC) Utilization, (vii) Innovation Diffusion Theory, and (viii) Social Cognition Theory [55,172]. The result of this process is the UTAUT (Unified Theory of Acceptance and Use of Technology) model which has been used in acceptance of robots [55,105,135]. It stated the influences of (i) Performance Expectancy, (ii) Effort Expectancy, and (iii) Social Influence, to be the direct determinants of Intention to Use or Behavioural Intention (please refer to Fig. 5b) [55,105,135,172]. 3.3. Robots and network systems involved in user–robot personality matching and human-friendship modelling In terms of human friendship estimation model, especially for humanoid and communication robots, based on the analysis of non-verbal HRI and inter-human interaction, Kanda et al., in 2008, proposed a model for estimating human friendships in the presence of a humanoid robot [87]. This is because they found that the different appearances of robots did not affect the participants’ verbal behaviours but did affect their non-verbal behaviours such as distance and delay in participants’ responses and the like [86,87]. This model is a further research done by Kanda et al. as well in 2006 [77] and 2007 [89] on previous estimation models developed. (See Table 3 for detailed comparisons). See Fig. 2a and b for the differences of the two humanoid robots, i.e. Robovie [82,86] and ASIMO [139]. These differences are explained by two factors – impressions and attributions [86]. Fig. 3 shows the environment and positions for what have been carried out by the participant and the two different robots in the experiments. Partner robots and screen agents (see Figs. 4a–c) have been used extensively because they act as human peers in everyday life. They perform mental and communicational support for humans as well as for physical support [102]. Pet robots (see Figs. 4b and 4c) have been used successfully in mental therapies for the elderly [55,82,161]. The conversational ability (by vocal) of robots helps humans to retrieve information through a computer network, and creates friendlier relationships with humans. Partner robots facilitate effective multimodal communication in order to complete an arbitrary set of tasks together with humans [82]. In contrast, Kanda et al. in 2010, created a robot system in a shopping mall but it detected a person with floor sensors to initiate interaction, and the robot was just partially tele-operated to avoid the difficulty of speech recognition. The HRI effect was shown to be even better [91]. 3.4. Modelling approaches and models involved in behavioural adaptation to model HRI According to Tapus’ and Mataric’s research work in 2008, behavioural adaptation is another recognized challenge to promote a more efficient HRI. This is because creating robotic systems which are capable of adapting their behaviours to user’s personality, user’s preferences, and user’s profile to provide an engaging and motivating customized protocol is a challenging target. This applies especially when working with vulnerable user populations, interaction zones or proxemics that include intimate, personal, social and public (see Fig. 9) [47,167]. Developments in the last decade in the field of robotics have ushered the interactive robots to be used in socially assistive applications [20,21,30,61,63,87,165]. Shibata and Tanie in

Table 3 Strengths, weaknesses and uniqueness of the assessment methodologies to assess human–robot interaction. Characteristics of the methodologies applied for longterm modelling of HRI

Author(s) (key authors only)

Type(s) of Model(s) used on assessing the HRI

Weaknesses (specific Assessment Methodologies applied)

Year(s) of each research

Heerink [55] Hennington and Janz [66] De Ruyter et al. [135] Heerink et al. [58,64]

UTAUT Model

(1) UTAUT model was applied to robot technology in a limited study. So, model needs adaptation in the sense of extension, modification or both; (2) must take the notion of social acceptance into deep consideration that complements technology acceptance; (3) Evaluation is based on the accuracy in predicting social acceptance; (4) it is a subjective approach

2005 [135] Adapt to the specific requirements of evaluating social 2006 [58] assistive robots through the five selected constructs, which are exactly the standard constructs of UTAUT 2006 [64]

Heerink et al. [57,62]

Almere Model

Gonsior et al. [47]

2010 [57,62]

(1) Godspeed Key Concepts is not an objective method, 2008 [3] this incurs bias; (2) There are certain overlap between 2009 [2] some concepts, such as in between anthropomorphism 2011 [47] and animacy, and the like; (3) it is extremely difficult to determine the ground truth, e.g. how anthropomorphic a certain robot is. So, this modelling approach may not be robust enough

(1) Almere Model is very appropriate for the elderly group’s assessments; (2) Participants’ self-assessment responses are used for the convergent validity of evaluation models used. Hence, more applicable generally to most types of HRI, mainly for elderly (1) Emphasizes the need for standardized measurement tools for HRI; (2) The five Godspeed questionnaires use 5point scales to help robots’ creators on their development; (3) The five consistent questionnaires that use semantic differential scales, and psychological measures are taken into account

Yanco et al. [182] Incorporating HCI/CSCW into Human–Robot Interaction Hayashi et al. [51– (HRI) Model; (or) Incorporating HCI alone into HRI Model 53] Kanda et al.[84] Jacobsson et al. [73,74] Pateraki et al. [126]

(1) Prior research on the personality issues may be required before applying this model; (2) Adaptation process may take quite a long time as a large number of parameter values and the role of robot’s personality in the hands-off or other therapy, research or entertainment processes have to be investigated; (3) focus on the relationship between the extroversion–introversion of the robot and the user and the robot’s ability to adapt its behaviour may be tedious

2002 [84] (1) Provides guidelines for developing interfaces for HRI; 2004 [182] (2) These assessment guidelines are used as frameworks for future assessments; (3) While robots act as passive2005 [52] social media, they are assessed just based on their 2007 [53] abilities to convey information in the public, no interaction with humans is needed 2007 [74] 2008 [51] 2008 [73] 2014 [126]

Tapus and Mataric [166–168] Bickmore and Schulman [10] Brave et al.[12] Tamura et al. [165] Dautenhahn et al. [30] Groten et al. [49] Hu and Loo [70]

(1) HRI Models;

(1) Prior research on the personality issues may be required before applying this model; (2) Adaptation process may take quite a long time as a large number of parameter values and the role of robot’s personality in the hands-off therapy or similar therapies have to be investigated; (3) focusing on the relationship between extroversion–introversion of the robot and the user is tedious; (4) clarification on humans’ intentions through haptic channel or similar model and the like is needed

2004 [165] (1) Behavioural adaptation model is capable of adjusting its social interaction parameters toward customized 2005 [12] rehabilitation therapy; (2) Promotes robot behavioural adaptation; (3) Learning approach will adapt robot’s 2006 [30] behaviour to better model user’s personality. In recent 2006 [166] year 2013, Haptic channel is used for humans’ intention 2006 [168] integration, while this year 2014, decision making model for intelligent agent is used 2007 [10] 2008 [167] 2013 [49] 2014 [70]

Moshkina and Arkin [113] Arkin et al. [1]

(1) This emotion and personality [113] as well as the (1) Integrated Model of Personality and Affect (TAME) (2) (1) These models may not be reliable because self-report 2003 [1] ethological and emotional models [1] have combined Ethological Model, and (3) Emotional Model data from the questionnaires given is subjective; (2) due to technical difficulty of the studies, sample size taken 2005 [113] these four areas of affect, i.e. TAME, to influence robotic

(2) User Personality Matching Model or User–Robot Personality Matching Model (3) Behavioural Adaptation Model, such as through Haptic channel or the like

311

(continued on next page)


Bartneck et al. [2,3] Godspeed Five Key Concepts Modelling

This model is very subjective and can be even more subjective than UTAUT model as they use participants’ self-assessment questionnaires to obtain convergent validity for HRI behavioural measurements

2007 [66] 2010 [55]

312

Table 3 (continued) Author(s) (key authors only)

Type(s) of Model(s) used on assessing the HRI


Year(s) of each research

behaviour; (2) Increases the ease and pleasantness of HRI

was rather small; (3) the chosen physical platform, designed for entertainment, may be a more decisive factor than differences in the robot behaviour. So, bias Leite et al.[101] Author(s) (just key Methodologies of Assessment for Long-Term Modelling authors) of HRI

2013 [101] Year(s) of Uniqueness of the Characteristics of these methodologies completed applied for long-term modelling of HRI research

(1) The generality of their research findings can be limited, i.e. model may not be appropriate for every HRI; (2) Robot’s capability for long-term interaction is limited as information that the robot can provide is of limited resources. So, HRI is restricted mostly to the non-verbal communication types

2004 2004 2006 2007 2008

[90] [78] [77] [89] [87]

(1) Interaction with robots in real-world setting; (2) Analyze interaction among children with robot from their non-verbal interactions; (3) Interactive robots have the fundamental ability to socially communicate with humans; (4) Friendship Estimation Model is a good approach for a social robot to understand human relationships (1) Empathic or autonomous HRI mobility effect needs to 2010 [27] (1) This model applies a good ethnographic study in the real-world settings; (2) Solves the doubts of affective be selected carefully as it is under the risk of having the 2012 [100] interactions with assistive social robots or mobile robots; opposite effect; (2) Bias may take place in the target application scenario; (3) children’s specific preferences 2013 [101] (3) Presented a first evaluation of an autonomous mobile or social robot capable of recognizing the user’s affective seem to influence the ‘‘degree of empathy’’ that social states and displaying appropriate empathic behaviour; robots should be endowed with (4) This modelling is important for designing empathic robot companions or autonomous mobile robots

Leite et al. [100,101] Cramer et al. [27]

Empathic or Autonomous HRI Model that includes an affect detector which allows the robot to infer the valence of the feeling experienced by the participants. For Cramer et al.’s [27] research, Likert-type and semantic differential scales are used to measure the robot’s perceived empathic ability and etc. For Leite et al.’s [100] research, no affect detector is used, but a series of online survey is used as the assessment measures

Yanco et al. [182] Olsen and Wood [123] Shiomi et al. [150] Zheng et al. [183– 185] Glas et al. [43,44] Melin et al.[108] Melendez and Castillo [107] Castillo et al. [18] Wang and Young [178]

Human–Robot Team (HRT) Model that involves multiple social robots and a specific design framework where a system is developed in which a single operator can simultaneously control multiple robots in conversational interactions with users. Human-Control of mobile robots model involves type-2 or type-1 fuzzy tracking controllers

(1) Coordination between autonomy and operation is often difficult in real applications of social and/or mobile robots; (2) Deployment of autonomous mobile and social robots often causes unnecessary time and effort; (3) failure of the robot team can often take place; (4) a lot of efforts are expended on the automation issue; (5) operator must perform very well for timing and simultaneous control

2011 [184] (1) Tele-operation of multiple autonomous mobile and/or 2012 [43] social robots – unique challenges posed by remote operation of multiple social robots, conducting multiple 2012 [44] interactions at once; (2) This modelling approach 2012 [18] describes the general system requirements in four areas and shows the effects of their system through 2013 [107] simulations and a laboratory experiment based on real2013 [108] world interactions; (3) more optimal autonomous control 2013 [183] by type-2 and type-1 fuzzy tracking controllers under perturbed torques; (4) more optimization of fuzzy 2014 [178] integrator for mobile robots 2014 [185]

Mutlu et al. [116,117] Cassell et al. [16] Staudte and Crocker [157] Mora et al. [109] Sakai et al. [140] Pateraki et al. [126] Murakami et al. [114]

Human Gaze Model [16,116] and Human-like Gaze Behaviour [116] Referential Gaze Model with Joint Attention Conversational Gaze Model [117] Automatic Gaze Control and 3D Visual Model [109,140] Visual Estimation of Pointed Targets [110,114,126,140]

(1) Highly technical mechanisms are required to ensure the coordination of the gaze models; (2) Controlled gaze cues may face difficulties to integrate with the unfolding speech and the gaze movements; (3) Coordination and synchronies may have mismatching problems in the gaze mechanisms applied to HRI

1994 [16] 2006 [116] 2011 [157] 2012 [117] 2013 [109] 2013 [140] 2014 [110]

(1) These gaze models are improved by researchers through various types of gaze mechanisms over the years; (2) Referential gaze model suggests that artificial agents are similar to human agents, thus validating joint attention mechanisms; (3) Conversational gaze model affect humans’ rapport with robot, feelings of group spirit with their conversational partners, and also their attention on tasks


Human Friendship Estimation Model is designed based Kanda et al. [77,78,87,89,90] on 3 three design principles: (1) it calls children by name; (2) it adapts its interactive behaviours for each child based on a pseudo-development mechanism; (3) it confides its personal matters to the children who have long interactions with robot


Characteristics of the methodologies applied for longterm modelling of HRI

Morales et al. [110] Hall [50] Kamasima et al. [76] Kanda et al. [80,82,86] Kozima et al. [96,97] Cooney et al. [25,26] Frank et al. [39]

2014 [114] 2014 [126] Psychological Model and Therapeutic Model (for research, therapy and entertainment) – based on Proximity Theory in Psychology, and/or related theories or principles

(1) Limited applications as certain therapeutic models only apply on children with Autism, Asperger’s syndrome or the like; (2) May take a long time to establish interpersonal coordination and interactional synchronies with the participants before non-verbal behaviours formulations can be conducted

1990 [50] 2004 [76] 2004[80] 2004 [82] 2005 [97] 2008 2009 2014 2014 2014

(1) Interaction with robots in real-world settings; (2) Social robot, e.g. Keepon, conducts non-verbal interactions with users, help researchers indirectly assess the HRI based on the psychological or therapeutic models; (3) Robot’s design and behaviour is effective in therapeutic purposes and sharing mental states with participants – so, good to be used as learning approaches for HRI

[86] [96] [25] [26] [39] D.Y.Y. Sim, C.K. Loo / Information Sciences 301 (2015) 305–344 313

314


Figs. 2 and 3. (2) Humanoid robots: (a) Robovie (Kanda et al. [82,86]) and (b) ASIMO (Sakagami et al. [139]), both of them are categorized as the service robots, under assistive social robots. (3) Environment and positions for the experiments done by the humanoid robots, i.e. Robovie and ASIMO (Kanda et al. [86]).

Fig. 4a. In terms of robot’s behavioural adaptation, different assessment methodologies have been used to continuously monitor and assess the satisfaction of users toward HRI. For the above robots and screen agents, Huggable is assessed mainly on its companionships, while Homie on its communication companionship. Annie, iCat and ISH software agent are assessed mainly on the monitoring controlling devices providing information, while Care-o-bot on its butler guide physical aid, ISH-Joy on both controlling devices and physical aid butler (Heerink [55]).


315

Fig. 4b. The PARO companion robot (Riek and Robinson [131]).

Fig. 4c. The huggable robot (Stiehl et al. [160]).

Fig. 5a. Unified Theory of Acceptance and Use of Technology (UTAUT) model used during implementation [55].

2001 developed a seal-like robot, named Paro (see Fig. 4b), for therapeutic purposes and demonstrated its social effect for encouraging communication among inpatients and caregivers [87,147]. In a similar vein, Kozima et al. in 2005 and 2009, placed a remote-controlled robot, Keepon, in a day care centre for developmental disordered children to encourage their social behaviours [87,96,97]. This paper is reviewing all the assessment and evaluation methodologies for therapy, research and entertainment purposes. Bartneck et al. in 2009 highlighted that the more animated the face of the robot, the more likely it is to attract the attention of a user [2]. From Figs. 4b and 4c as shown, PARO (refer to Figs. 13a and 13b) is the robotic seal [131,173], as well as the Huggable robot [160], present themselves embodied within stuffed animals, and behave as one might expect an animate toy or pet to act. The robot’s behavioural adaptation is one of the main assessment methodologies

316


Fig. 5b. UTAUT model: direct influences and moderating factors are represented [55,172].

Fig. 5c. Humanoid robot, Honda’s ASIMO, is telling a Japanese fairy tale to two listeners [116] – modelling is one of the assessment methodologies to monitor, manipulate and assess the robot’s human-like gaze behaviour [116] – (see Table 3).

Fig. 5d. Clustering of the four gaze locations used by the storyteller [116] – one of the assessment methodologies to assess the robot’s human-like gaze behaviour [116] – (see Table 3).

Fig. 5e. Overview of networked robot system [150].


317

Fig. 5f. Examples of path information [150].

that researchers should incorporate in their learning approaches to model a better HRI. Fig. 4a shows a series of different types of robots and screen agents categorized by Heerink in 2010 while different combinations of assessment methodologies have been used under certain evaluation methodologies during long-term modelling of HRI [55]. 4. Assessment methodologies that have been carried out to assess and long-term model the HRI The original TAM model has been extended with more influences that were found to influence Intention to Use or Usage [55] (see Fig. 5a). Venkatesh and Davis in 2000 [171] reintegrated the concept of Subjective Norm, and this model is usually referred to as TAM-2 [55,171]. As mentioned above, there should be multiple methods of assessments within a single method of evaluation. Some of the methods of assessments were adopted and/or modified from existing scales used in Psychology, Social Sciences, and other HRI researchers [5,6]. From the research work done by Bethel and Murphy in 2009, multiple signals (within a single method of evaluation, i.e. psycho-physiological study) were used for obtaining reliable and accurate results. Correlations were conducted between the different signals to determine the validity of participants’ responses [5,8]. To further support the statement above, i.e. ‘within a single method of evaluation, there should be multiple assessment measurements to be utilized’, Bethel et al. in 2009 had proven the results in their research work [6]. Multiple assessment measurements are conducted within a single method of evaluation for HRI [5,6,8]. In Section 3, we have reviewed different types of modelling approaches and measurement tactics for Assessment Methodologies. In this section, we review all the combinations of assessment methodologies that have been done by the past researches in assessing and long-term modelling the HRI. 4.1. Human gaze models with human-like gaze behaviour, referential gaze model and walking gaze models for HRI Fig. 5c shows the research done by Mutlu et al. on Honda’s humanoid robot ASIMO, i.e. storytelling requires ASIMO to be aware of its audience and be able to direct its gaze in a natural way. They explored how human gaze can be modeled and implemented on a humanoid robot to create a natural and human-like behaviour for storytelling [116]. This assessment methodology is based on a gaze model which integrates data collected from a human storyteller and a discourse structure model developed by Cassell et al. [16,116]. Mutlu et al. used this model to direct the gaze of ASIMO (see Figs. 5c and 5d), as they recited a Japanese fairy tale using a pre-recorded human voice. They assessed the efficacy of this gaze algorithm by manipulating the frequency of ASIMO’s gaze to assess whether the participants evaluated the robot more positively and did better on a recall task when ASIMO looked at them more [116]. In 2006, Mutlu et al. further illustrated in their research by stressing that there are many commonalities between human–human communication and human–robot communication. The assessment methodology used is also based on modelling the human gaze and subjective evaluation of human-like gaze behaviour. They explore how human gaze can be modeled and implemented on a humanoid robot in order to create a natural, human-like behaviour for storytelling. Hence, their research further confirms the usefulness of using Gaze Models as one of the assessment methodologies for HRI. In addition to the research in 2006 on modelling and evaluation of human-like gaze behaviour [116], Mutlu et al. in 2012 (see Fig. 12b), evaluated the HRI based on the conversational gaze mechanisms for human-like robots [117] (please refer to Table 3 for different gaze models implemented over the years). In 2013, i.e. last year, Mutlu et al. used more specific coordination mechanisms for achieving a much better human–robot collaboration through collaborative manipulation [119]. In 2014, i.e. this year, in a similar research vein, Pateraki et al. formulated a novel approach which takes into account the prior information about the location of possible pointed targets, based on the fact that in most applications, it is the pointed object, rather than the actual pointing direction which is important. They addressed an important issue in HRI, that of accurately deriving pointing information from a corresponding gesture [126]. To decide about the proposed object, as being more

318

Table 4 Combinations of assessment and evaluation methodologies for HRI (in chronological order for key authors). Model(s)/Test-bed(s) Protocol(s) used

Strengths In terms of Evaluation

In terms of Assessment

Weaknesses In terms of Evaluation

Bartneck et al. [3] Bartneck et al. [2] Gonsior et al. [47]

Godspeed Five Key Concepts Modelling

(1) Emphasizes the need for standardised measurement tool; (2) Questionnaires use 5point scales to help robots’ developers or evaluators; (3) Questionnaires are consistent as using differential scales

(refer to Table 3 – see Page 21, 3rd row, last column)

(refer to Table 3 – see Primary evaluation Page 21, 3rd row, 3rd methodology of using column) questionnaires alone is subjective, and hence bias may take place

Yanco et al. [182] Olsen and Wood [123] Shiomi et al. [150] Zheng et al. [184,183,185] Glas et al. [43,44] Castillo et al. [18] Melin et al. [108] Melendez and Castillo [107]

(1) Human-control of Multiple Robots; (2) Teleoperation of Multiple Social Robots Model; (3) Human–Robot Team (HRT) Modelling

The Primary Evaluation Methodologies used are the question-and-answer dialogue questionnaires and task performance metrics works very well with HRT model and teleoperation multiple robots model

(1) Able to show the effectiveness of their system based on realworld setting or simulations; (2) This assessment model is very suitable for assessing multiple robots system

(1) Covers only single or limited round of dialogue; (2) Task Performance Metrics are more suitable for humanoid robots; (3) Not very suitable for oneto-one HRI; (4) Simulation results are based only on user studies. So, results are not real-world specific

De Ruyter et al. [135] Heerink et al. [58,64] Bartneck et al. [2]

(1) UTAUT Model or Almere Model; (2) iCat robot (as a test-bed for

(1) Use UTAUT model as a (1) The questionnaires At least three very used are specific to measure of iCat robot prominent primary evaluation methodologies technology acceptance in certain context only, or


(1) Parameters are set for a specific context, hence HRI is assessed on very limited topics; (2) Suit mostly for humanoid robots; (3) this methodology does not model random errors of automation. So, not very accurate; (4) Timing is of mission critical

Uniqueness of the Research Work (Testbed(s) and/or protocol(s) used) – Contributions of this modelling approach to the Society (1) Testifies the five key measurement concepts while evaluating and assessing the HRI; (2) These five key concepts are thorough in reaching a good overall evaluation score and assessment model in many kinds of HRI; (3) It is suitable for almost any kind of HRI; (4) Has a good combination of evaluation and assessment methodologies for HRI (1) Tele-operation of multiple social robots is usually very efficient as timing critical; (2) Both needed for humancontrolling or teleoperating of the robot and estimating the interaction success; (3) Very suitable in illustrating the dynamics of the system and the effects of varying parameters; (4) Very useful in designing and tuning systems for the real-world deployment; (5) more optimal autonomous control by type-2 and type-1 fuzzy tracking controllers under perturbed torques; (6) more optimization of fuzzy integrator for mobile robots

(1) UTAUT model applied (1) Demonstrated the relevance of social is a modified version, researchers can only draw intelligence in HRI


Authors/Years of Research (key authors only – ascending chronol. order)

tentative conclusions from this measurement; (2) More effective ways of achieving social intelligence for a better perception are still yet to explore

are used, i.e. specific questionnaires developed as well as interviewing and observations. All these are to evaluate the perception of HRI social intelligence

the workplace – a standardized model that covers all the 5 constructs; (2) Able to show human-like behaviours, so, socially intelligent

questionnaires applied in limited contexts. Hence, not standardised enough; (2) Questionnaires method is subjective, so, may incur bias

(1) Psychological and Therapeutic Modelling; (2) Keepon (a robot) as a test-bed for Social Intelligent Modelling [54,55]

(1) Ethnographic Observation of Keepon’s rhythmic interactions is a unique primary evaluation methodology for HRI. (2) Non-primary evaluation on non-verbal interactions is a very suitable way to evaluate HRI for autistics

(1) Computational model of rhythmic synchrony developed is able to convey robot’s attention and emotion; (2) rhythmic synchronies and interactions are very suitable for Psychological and Therapeutic Modelling

(1) Limited application to (refer to Table 3 – see autistics or similar mental Page 22, last row, 3rd disorders only; (2) column) Primary evaluation by ethnographic observations and other non-primary evaluation methodologies can be difficult as too specific models

(1) Appropriately designed robot facilitates dyadic interaction between an autistic child and robot, as well as triadic interaction among autistic children and caregivers; (2) Movement and dance can have therapeutic effects, (3) Behavioural observations, such as ethnographic observations and other evaluation methods, prove that interactive robots, such as Keepon [54,55], can facilitate children’s social interactions

Heerink et al. [57,62] Hall [50] Kamasima et al. [76] Kanda et al. [80,82] Kozima et al. [97] Kanda et al. [86] Kozima et al. [96] Cooney et al. [25,26] Frank et al. [39]

Authors/Years of Research in Model(s) with Testchronological order (key authors only) bed(s)/Protocol(s) used

Strengths of these Strengths of these Evaluation Methodologies Assessment Methodologies

Weaknesses of these Weaknesses of Evaluation Methodologies Assessment Methodologies

Uniqueness of the Research Work (Testbed(s) and/or protocol(s) used) – Contributions of this modelling approach to the Society

Tamura et al. [165], Brave et al. [12], Dautenhahn et al. [30], Tapus and Mataric [166,168,167] Bickmore and Schulman [10] Groten et al. [49], Hu and Loo [70]

At least three Evaluation (refer to Table 3 –see Page Methodologies are used, 21, 5th row, last column) i.e. (1) Self-Report evaluation (2) Behavioural observations by the users on the

(refer to Table 3 –see Page (1) Primary evaluation method of using self21, 5th row, 3rd column) report measures is subjective, bias may be incurred; (2) The HRI and Personality Matching

(1) Models facilitate assistive social robot systems to aid people in daily lives; (2) Have novel multidisciplinary collaboration including

(1) Behavioural Adaptation Model; (2) HRI Model; (3) User– Robot Personality Matching Model; (4) Haptic channel; (5)

319

(continued on next page)


computational characters; (2) Able to show an increase in the perceived social intelligence; (3) Able to greatly substantiate that iCat and the its behaviours have a significant effect on the satisfaction level with the embedded systems, technology acceptance, and sociability towards the system; (4) Able to explore the concept of social intelligence; (5) Behavioural observation is an effective primary evaluation

social intelligence)



Weaknesses In terms of Assessment

In terms of Evaluation


Uniqueness of the Research Work (Testbed(s) and/or protocol(s) used) – Contributions of this modelling approach to the Society

Decision Making Model for Intelligent Agent

robots, and (3) Psychophysiological Measures – all of these methodologies work very well with all the three Assessment Models stated, especially for the Behavioural Adaptation Model; (4) Haptic channel for HRI Intention Integration

models applied are too specific for certain therapeutic purposes; (3) Models may take a long time to develop due to robot’s behavioural adaptation; (4) Although haptic channel enhances shared decision situations, it takes time to realties haptic HRI

cognitive psychology; (3) The role of robot’s personality in the handsoff therapy process or etc. focuses on the relationship between the extroversion– introversion of the robot and the user, and the ability of the robot to adapt its behaviour to the user personality and preferences; (4) Models are applicable to both children and adults, who are normal or with mental disorders, e.g. autistics who needs therapies

(1) HRI model with HCI and CSCW incorporated; (or) HRI model with HCI alone; (2) Robots as passive-social media

(1) HCI/CSCW evaluation (refer to Table 3 – see Page 21, 4th row, last techniques, i.e. questionnaires are used column) for users’ perception; (2) Involves observation of robots and design of guidelines for developing HRI interfaces. So, specific enough;(3) Metrics to evaluate task performance are very HRI oriented, i.e. specific enough

(refer to Table 3 – see Evaluation criteria is of narrower scope because it Page 21, 4th row, 3rd column) is limited to the user interfaces such that robots acting as passivesocial media, or in user interfaces aided by HCI and CSCW, i.e. narrower scope evaluation on interfaces

Incorporating CSCW and/ or HCI into HRI model – (1) Model shows initial guidelines for designing interfaces for HRI, based on robots acing as passive-social media or in CSCW; (2) This model analyzes pre- and postevaluation debriefings to develop guidelines for developing interfaces for HRI – these guidelines are used as frameworks for assessing and evaluating robots; (3) HRI Evaluation is beyond usability evaluation – operator– robot pairing and the like is enhanced by HCI and CSCW


Kanda et al. [84] Yanco et al. [182] Hayashi et al. [52] Hayashi et al. [53] Hayashi et al. [51] Jacobsson et al. [74,73] Pateraki et al. [126]

Model(s)/Test-bed(s) Protocol(s) used

320

Table 4 (continued)

(1) Temporal Awareness Model or Timing Model; (2) Robovie or Robovie II – (humanoid robot is used as a test-bed for embodiment); (3) iCat robot

(1) As this model involves human and/or robot task performance metrics, primary evaluation methodology is not subjective and so, much less biased; (2) very suitable for evaluating multi-robot system, i.e. team scoring and group HRI evaluation.

Author/Year (Key Authors only)

Model(s) Test-bed(s)/ Protocol(s)

Strengths

Riek and Robinson [131] Gockley et al. [45] Sung et al. [163] Mutlu et al. [118,117] Satake et al. [142] Liu et al. [104] Sorbello et al. [156]


(refer to Table 2 – see (1) Societal Acceptance Modelling for HRI; (2) Page 20, 1st row, both 3rd Technology Acceptance and last columns) Modelling (TAM); (3) EOC formula

Jacobsson et al. [74] Jacobsson et al. [73] (1) HRI model with HCI (refer to Table 2 – see Shihab and Sim [148] Shihab et al. and CSCW incorporated Page 20, 3rd row, both 3rd on interfaces; (2) Open and last columns) [149] Sim [155] exploring robot platform, i.e. see Puck and e-Puck; (3) Glowbots as demonstrators – used as testbeds

(1) Usually works well with HRT model or models involving multirobots systems; (2) Incorporates well with the task performance metrics; (3) This model is good for tele-operating multi-robot system as timing is critically under proper control

(1) Task performance metrics is more suitable for tele-operated HRI; (2) Task performance metrics may not be very suitable for non-humanoid robots; (3) Improper timing may incur errors.

(1) Technical difficulties may incur problems on the timing control; (2) Need expertise to work on the tele-operation interface design; (3) Controls a network that maintains timing can be resource consuming and tedious.



(1) This model is of very fair approach; (2) works well with the HRT models and other multi-robot assessment models, i.e. really good for teleoperation control; (3) particularly good when more than one robot or one human are involving in the HRI; (4) more optimal autonomous control by type-2 and type-1 fuzzy tracking controllers under perturbed torques; (5) more optimization of fuzzy integrator for mobile robots Uniqueness of Research


Contributions of this modelling approach to the Society

(refer to Table 2-see Page (1) Consistent with Human-Centered Design, 20, 1st row, 4th column) i.e. technology acceptance is directly related to users’ mental models;(2) Assessment is straightforward, i.e. measures how easy a user identifiers a robot’s type, etc.

(1) Bias may be incurred from the users’ EOC; (2) Although think aloud method can be used, it is still a subjective method; (3) Users often feel reluctant to classify the robot, i.e. EOC, when they feel hard to tell apart its type

(1) Societal Acceptance Modelling with the Ease of Classification (EOC) allows easy quantifiable metric; (2) EOC formula is very straight forward and easy to be worked out; (3) This type of modelling approach is simple as it does not involve any tedious adaptation for the robot during HRI

(refer to Table 2 – see (1) This method is suitable for any kind of Page 20, 3rd row, 4th HRI involving user column) interfaces, both to humanoid and nonhumanoid robots; (2) Suitable for HRI involving one or multiple robots

(1) Assessment is not thorough or robust enough if compared with TAM or Godspeed Five Key Concepts as HRI is assessed mainly on Glowbots’ patterns; (2) May take a long time to assess HRI with HCI and CSCW platforms

(1) This model is a good learning mechanism involving open platforms and/or user interfaces especially for nonhumanoid robots, i.e. Glowbots or etc.; (2) Continuous improvement from the non-primary evaluation on HRI can be done by users directly on the open exploring robot platforms. So, convenient


Kanda et al. [83] [82]Shiwa et al. [153] Shiwa et al. [152] Glas et al. [43,44] Castillo et al. [18] Mora et al. [109] Melin et al. [108] Melendez and Castillo [107]

(continued on next page) 321

Model(s)/Test-bed(s) Protocol(s) used

Mutlu et al. [116] [117] Cassell et al. [16] Staudte and Crocker [157] Mora et al. [109] Sakai et al. [140] Morales et al. [110] Murakami et al. [114] Pateraki et al. [126]

(1) Human Gaze Model [16,116]; (2) Referential Gaze Models with Joint Attention [157]; (3) Conversation-al Gaze Models [117] with 3D Model [109]; (4) Visual Estimation of Pointed Targets [110,114,126,140]



(refer to Table 3 – see At least two primary evaluation methodologies Page 22, 4th row, last are applied since mainly column) involves observation, questionnaires and/or gesture imitation, task performance metrics



(refer to Table 3 – see (1) As gaze models are Page 22, 4th row, 3rd mainly used for column) humanoid robots, not very suitable for nonhumanoid robots; (2) Evaluation may need time and IT expertise

Uniqueness of the Research Work (Testbed(s) and/or protocol(s) used) – Contributions of this modelling approach to the Society (1) Modelling has little bias as not involving any pure subjective evaluation from users. (2) Confirms that by establishing eye-contact or observing and imitating gestures, humans can greatly increase utterances; (3) Confirms that gaze is affecting task performance in learning approaches for HRI



322

Table 4 (continued)


323

sophisticated research work than what Mutlu et al. did in 2006 [116] and also Ono et al. did in 2001 [82,124], Pateraki et al. in this year, i.e. 2014, proposed using the Dempster-Shafer theory of evidence to fuse information from two different input streams [126]. Preceding research to support this, from the research done by Ono et al. in 2001, by establishing eye contact, observing and imitating gestures, humans can greatly increase the understanding of others’ utterances during HRI [82,124]. In addition, from the studies done by Kidd et al. in 2006 [93] and Taggart et al. in 2005 [164], it has been concluded that, for companion robots (such as the Japanese toy robot dogs Aibo and Paro – see Figs. 4b and 4c), which are assistive social type robots, not only functionality, but also form and material does matter a lot in terms of acceptance and effects of them toward humans [55,93,164]. The results from the assessment methodologies matched the predictions in literature, i.e. gaze is also shown to affect task performance in learning approaches [41,116,125,146]. Mutlu et al. found that participants performed significantly better in recalling ASIMO’s story when the robot looked at them more. The assessment methodology results also showed significant differences in how men and women evaluated ASIMO based on the frequency of gaze they received from the robot [116]. In year 2012, Mora et al. incorporated automatic gaze control and 3D spatial visualization, a more sophisticated evaluation methodology was used [109]. In 2013, i.e. last year, Sakai et al. created a motion design of interactive small humanoid robot with visual illusion – this again further proves that gaze models with human-like gaze behaviours are important!! [140]. The gaze and gesture algorithm for ASIMO [116] is done by building on results in the literature for avatar gaze [16]. Again, this is another good combination of assessment and evaluation methodologies applied to HRI, preceding the researches done by Kanda et al. in 2008 [86,87]. When dealing multiple social robots, the task performance metrics are used, especially where teams or groups are being evaluated and/or more than one person is interacting with one or more robots [5,14,116,118,158]. To illustrate this, the experiments done by Shiomi et al. in 2009, a networked robot system (see Figs. 5e and 5f) was developed that coordinates multiple social robots and sensors to provide efficient service to customers in a shopping mall. This networked robot system directs the tasks of robots based on their positions and people’s walking behaviour [150]. In the same vein to support this research, in this year, i.e. 2014, Morales et al. constructed a walking together – side by side walking model for an interacting robot [110], and Murakami et al. created a framework for walking side-by-side without knowing the goal, i.e. destination unknown, for HRI [114]. All these work in 2014 [110,114] again confirm that people’s walking behaviour people’s walking behaviour, gaze models and human-like gaze behaviours are important for long-term modelling the HRI (see Table 4 for more details). 4.2. UTAUT and Almere Models – the learning approaches adopted as one of the Technology Acceptance Modelling (TAM) on HRI According to Heerink [55], the Unified Theory of Acceptance and Use of Technology (UTAUT) model has been applied to robot technology in a limited study, and not just for elderly users (refer to Figs. 5a and 5b). It was also found that many research studies showed how the UTAUT model needed adaptation in the sense of extension, modification or both [55,58,60,64,66,172]. This has been supported by the research done by Hennington and Janz [66]. This is because they showed the effects of applying UTAUT model in a healthcare context while the model needed adaptation for physician adoption of electronic medical records [66]. As a review finding from Hennington’s and Janz’s work in 2007 [66], as well as a conclusion done by Heerink’s research in 2010 [55] it is that we must take the notion of social acceptance into consideration as a concept that complements technology acceptance. This implies that research on robot and agent acceptance can be subdivided into two areas: (1) the acceptance of the robot in terms of its usefulness and ease of use and (2) the acceptance of the robot as a conversational partner with which a human or pet like relationship is possible (social acceptance) [55]. In addition, Heerink also found that the experiments with companion type robots (such as Paro and Aibo) were more focused on social acceptance, while the experiments with service type robots (such as Pearl and iCat) focused more on the functional acceptance, i.e. the acceptance of the robot regarding its functionalities. Hence, to be able to obtain a complete view on acceptance of an assistive robot, researchers and robot developers need a model that enables us to explore both the social and functional acceptance. This means that researchers also have to evaluate this UTAUT model based on the accuracy in predicting both [55,172]. In Figs. 5a and 5b above, Heerink [55] extended the UTAUT Model by several constructs to adapt this model to the specific requirements of evaluating social robots [55,172]. Besides UTAUT model, Heerink et al. built an Almere model (see Table 3) for measuring acceptance of assistive social agent technology by older adults in 2010 [57,62]. Acceptance methodology is traditionally a questionnaire, which replies on a Likert scale [57]. In terms of the Assessment Methodologies, relating conversational expressiveness to acceptance, TAM adds behavioural analysis to instrumentation. This enriches acceptance methodology [57]. 4.3. Human friendship estimation model – learning approaches adopted for assessment methodologies on HRI Kanda et al. in 2008 proposed a model for estimating human friendships in the presence of a humanoid robot, and this is based on the analysis of non-verbal inter-human interaction. They analyzed the video data based on an observation method, as a methodology to analyze the interaction among children and the robot [87]. This research is further to the research done by Kanda et al. as well in 2004 on friendship estimation model [78,90] for HRI. So, Kanda et al. are able to evaluate the established model for friendship estimation [87] (see Table 3). Past researchers suggested the importance of recognizing the relationships among what children have with other children in socially assistive applications [87]. Kanda et al. in 2004

324


conducted a study where Robovie (see Fig. 2a), their communication humanoid robot, interacted with elementary school children. The assessment methodology used is to analyze the video data obtained in the preliminary study and established a model for assistive social robots to recognize friendships among children from non-verbal interaction. The video data done were analyzed with an observation method which is very well-established in Psychology [87,90]. Kanda et al. in 2004 [78] reported their novel approach to develop an assistive social robot. Such a robot reads human relationships from their physical behaviours. They have developed an interactive humanoid robot that attracts humans to interact with it and, as a result, induces their group behaviours in front of it. In this approach, the robot recognizes friendly relationships among humans by simultaneously identifying each person in the interacting group. They conducted a two-week experiment in an elementary school, in which the humanoid robot, Robovie, demonstrated proven reasonable performance in identifying friendships among the children [78]. This ability to read human relationships is essential to for assistive social robots to behave socially [78,90]. 4.4. HRI Modelling with HCI alone OR with both CSCW and HCI incorporated Jacobsson et al. in 2008 used a platform, named see-Puck (see Fig. 7a), for exploring human–robot relationships. This seePuck is a round display module that extends an open robot platform, named e-Puck [73]. Glowbot is the first demonstration of robot constructed using the see-Puck platform [73,74]. As opposed to therapist hands-off robot shown in Fig. 6 [166], Figs. 7a and 7b show the group of interacting robots that uses their patterns to attract users’ attention and encouragement [73,74]. Since visualization by using Human–Computer Interaction (HCI) techniques is important [148,149,155], it is proposed that the modelling, especially on the Graphic User Interfaces (GUIs), may analyze the attractiveness of the user interface in terms of task analysis first, and then followed by goal analysis and lastly to scenario analysis [155]. The assessment methodologies of these visualization research include feedbacks done by the users, software developers and the like, as well as the users’ feedbacks on the HCI and/or HRI [73,74,148,149,155]. For the research done by Heylen et al., because there is no multiple robots or groups of subjects involved, choosing self-assessment method of evaluation is enough to evaluate the HRI [67]. So, in terms of evaluating HRI, certain subjective method of primary evaluation, such as self-assessment or self-report, is usually adopted as an enhancement [5,8,67]. Tables 2 and 4 show the uniqueness of Jacobsson et al.’s researches in using the Glowbots demonstrators on the platform, named See-Puck, as a learning mechanism for the non-humanoid robots to adopt while interacting with humans [73]. Table 2 illustrated the open robot platform implemented to further enhance the modelling of HRI. Hayashi et al. in 2008 constructed a robot system, named Manzai, as a passive-social medium (see Fig. 8a–c) during HRI, for HRI. In which, robots behave as if communicating by speech, while in fact the system exchanges information through a network that maintains communication timing [52]. The evaluation is done on the development of a multi-robot conversation system based on network communication. Based on the developed system, they implemented the Robot Manzai and compared its performance with Manzai performed by humans shown on video [52]. The assessment and evaluation methodologies done are based on the passive-social media, acted by the robots, besides Manzai (see Fig. 8d and e). The HRI model can be aided by Human–Computer Interaction (HCI), e.g. XML-based visualization system, or by both Computer-Supported Cooperative Working (CSCW) and HCI. Convergences of these HCI techniques can further improve the visualization effects on the GUIs [155]. 4.5. Human Robot Team (HRT) modelling, human-control of single or multiple robot system(s) for HRI Kanda et al. in 2004 evaluated interactive humanoid robots, such as Robovie, by comparing their body movements with subjective evaluation, which is based on the psychological method [82]. This is a combination of subjective evaluations with objective types of assessment methodologies. These authors intend to discover knowledge on embodiment that partner

Fig. 6 - (alone)

Fig. 7(a) - (in a group)

Fig. 7(b) - (in a group)

Figs. 6, 7a and 7b. Therapist Hands-Off Robot (Tapus and Mataric [166]). (7a) A group of interacting robots that uses their patterns to attract user encouragement [73]. The method of assessment used is mainly ‘Self-Assessments’ Evaluation method which is conducted mainly through observations as well as users’ feedbacks. (7b) GlowBots interact among themselves and with users to create interesting patterns [74]. The method of assessment used is mainly ‘Self-Assessments’ Evaluation method which is conducted mainly through observations as well as users’ feedbacks.

325


(a)

(b)

(c)

(d)

(e)

Fig. 8a–e. (a) Passive; (b) Interactive and (c) Passive-social: Robot, as a passive social medium, named Manzai, created by Hayashi et al. [12,53]. This figure tells us why multiple evaluation methods and assessment techniques are needed for this kind of HRI since the interaction is dynamic and may involve multiple subjects or more than one robot – (see Table 2). (d) Robot Manzai ‘‘Robovie and Wakamaru’’ and audiences at Expo 2005 [52] – (see Table 2). (e) Application of a passive social medium during the HRI [52] – (see Table 2).

robots can utilize to encourage humans to interact with them [82,83]. Last year, i.e. in 2013, to achieve this, Melin et al. used type-2 and type-1 fuzzy tracking controllers, under perturbed torques using a new optimization paradigm for autonomous control of mobile robots [107,108]. In 2012, Cazarez-Castro et al. designed type-1 and type-2 controllers via fuzzy Lyapunov Synthesis to have good control of mobile robots through non-smooth mechanical systems [18,19]. So, what has this trend of HRI technology advancement told us? Zheng et al. in 2013 and 2014, i.e. the latest research last year and this year to support the above, designed and implemented a human–robot team for social interactions, and then created a supervisory control of multiple social robots for conversation and navigation [183,185]. It is well known that during any conversation or in any interaction, a human immediately detects correspondences between their own body and the body of their partner. So, this suggests that to produce effective communication skills for an interactive robot during the HRI, its body should be based on a human’s body movements and this should be done with subjective evaluation [82,116]. The development of humanoid and interactive robots such as Honda’s [139] and Sony’s [42] is a research direction in Robotics. The concept of partner robot is rapidly emerging, and the evaluation of human–humanoid robot interaction is a concern for Kanda et al. in their research work in 2004, 2008, 2009 [79,82,86,88,121] till now. The previous research on HRI, which is often motivated by Cognitive Science and Psychology [170], has determined various interactive behaviours that the robot’s body should afford (see Tables 3 and 4). Figs. 11a and 11b show the various interactive behaviours of the humanoid robot with the human [82]. Fig. 2b and c show how assistive social robots, Robovie and ASIMO, are compared and contrast for HRI experiments [86]. 4.6. User–robot personality matching model, robot behavioural adaptation model and HRI model for the HRI Fig. 9 above shows exactly the HRI model adopted by Tapus and Mataric in 2007 as they posit that it is necessary to incorporate personality and empathy [167,168], during evaluation and assessments, in order to facilitate the HRI and robot behaviour selection [167]. Before any formal evaluation and assessment on HRI, we need to make sure that a social embodied robot must make appropriate use of the social space so that the users can feel safe, comfortable and in concordance with his or her personality preferences [167] (see Figs. 9 and 10a). Empathy can have really profound positive effects on users’ attitudes towards social robots [12,27,69,94,127]. So, responding to the user’s affective experience in a socially appropriate manner is considered really important issue in achieving user’s trust and satisfaction, as well as compliance to requests [10,12,27,30,165].

Fig. 9. HRI Model: interaction zones or proxemics proposed by Tapus and Mataric in 2007, i.e. intimate, personal, social, and public [167]. This figure tells us why multiple evaluation methods and assessment methodologies are needed for almost any kind of HRI.

326


Fig.10(a). Therapeutic and Psychological Modeling [97] Fig. 10a. Eye-contact (referring to each other’s mental states) of Keepon, and joint attention (sharing the perceptual information), that enable the interactants to exchange intention and emotion toward the target (Kozima et al. [96,97]) – (see Tables 2–4).

The experiments done by Tapus and Mataric’ to promote HRI Model are mainly addressing these two issues: (1) they investigate the user–robot personality matching; (2) by using the results of the first experiment, they refine the matching process between the user and the robot using their own adaptation algorithm. Preceding the research of Tapus and Mataric on the robot’s behaviour control and its architecture for therapeutic purposes [166,168], Sterling and Gaertner have shown a positive correlation between empathy and physiological indices (such as heart rate acceleration, palm sweating, and eye blinking) [159]. These physiological responses can also be used by the robot as a significant source of sensory information for real-time interaction and emphatic response [159,166]. So, using emulation capabilities of robot as one of the assessment methodologies, is a good HRI assessing and modelling approach. In contrast of this similar vein, in 2014, i.e. this year, Hu and Loo showed an assessment and modelling approach using a generalized quantum-inspired decision making model for intelligent agent [70]. This is one of the latest HRI models. 4.7. Empathic model for long-term modelling of empathic behaviours during HRI in real-world settings These assessment methodologies are assessing based on the idea that autonomous social robots capable of assisting us in our daily lives is becoming more real everyday [100]. In year 2013, Leite et al. supported this research using a specific empathic model [101]. In 2012 [100], i.e. the year before, Leite et al. indicated that autonomous social robots should have social capabilities so as to make our daily interactions with robots more natural. Their research findings suggest that the robot’s empathic behaviour affects positively how children perceive the robot. However, the weakness of this modelling is that the empathic behaviours should be selected carefully as under the risk of having the opposite effect. In addition, the target application scenario and the particular preferences of children seem to influence the ‘‘degree of empathy’’ that social robots should be endowed with [100]. Further to this research, there has been a growing interest in studying the interaction with robots, i.e. the HRI, in real-world settings (see Table 3). This includes the research studies done at homes [100,162], workplaces [100,115], elderly-care facilities (see Figs. 13a–13c) [100,136,173] or schools. The goal of the research done by Leite et al. in 2012 and 2013 is to qualitatively evaluate children’s reactions to an empathic robot in again, a real-world setting, by using an affect detector. This affect detector allows the robot to infer the valence of the feeling experienced by the children [100,101]. This is a good assessment methodology. In another emotional mimicry study done by Riek et al. in 2010 [132], it was found that most participants considered the interaction with the robot, i.e. the HRI, to be more satisfactory than participants who interacted with a version of the robot without mimicking capabilities [100,132]. In the same vein, more recently than Riek and Robinson in 2008 [131], Cramer et al. [27] in 2010, studied how empathy affects people’s attitudes towards robots. To support the assessment methodologies of HRI, for the evaluation methods incorporated to augment the assessment methodologies, Cramer et al. use Likert-type and semantic differential scales to measure the robot’s perceived empathic ability, trust (dependability, credibility) and closeness [27]. Different from Leite et al.’s research, no affect detector is used [100,101]. 4.8. Social acceptance modelling for HRI Riek and Robinson in 2008 [131] suggest Classification Ease as one of the assessment methodologies to assess the societal acceptance of robots. This is consistent with one of the core ideas in Human-Centred Design – the technology acceptance is directly related to the consistency with users’ mental models [131]. There are a variety of tools available for HRI researchers seeking to assess aspects of the societal acceptance of robots [48,131]. From most past researchers done to assess the societal acceptance of robots, there include: (1) ethnographic observation [38], (2) system response-time analysis [153], (3) common ground analysis [161], (4) embodiment measurement [29], (5) perceived enjoyment analysis [59], (6) comfort level analysis [95], (7) interaction profile analysis [133], and others [48,131]. For people to accept robots in social contexts, it is important


327

that the robots are easily classifiable, i.e. end users should be able to quickly and easily identify a robot’s type, role, and behavioural function. This is further supported by the societal acceptance research work done by Mutlu et al. [117–119] in the years 2006, 2012 and 2013. To support societal acceptance modelling for HRI, i.e. non-verbal behaviours and/or designs of robots and the like, Liu et al. in 2014, i.e. latest research this year, showed ways to train robot by teaching service robots to reproduce human social behaviour [104]. Again this year, i.e. 2014, Sorbellow et al. stressed their research in using telenoid android robot as an embodied perceptual social regulation medium to control natural human–humanoid interaction [156]. This research can be used as one of the latest research frameworks in social acceptance modelling for HRI in 2014. This can also be used as a flourishing channel for the research done by Satake et al. in 2013 that showed ways how a robot approaches pedestrians [142] (see Table 2 for the categorization of these 2013 and 2014 research work). Preceding this research, Riek and Robinson [131] proposed that for people to accept robotic agents socially, it is necessary for the robots to be easily classifiable. They constructed an Ease of Classification (EOC) Score formula. Although this EOC formula is grouped under evaluation methodology (see Section 5.2.2) because it is a score, continuous assessments and monitoring are still required for improving the EOC formula. Riek and Robinson expect that additional research in this social acceptance area will lead to a refinement of the EOC score formula and to the establishment of a reproducible testing methodology. Moreover, this formula has a few loopholes yet to be verified experimentally. Again, this confirms that Ease of Classification Methodology can be used as an Assessment Methodology (see Table 4), and also a Non-Primary Evaluation Methodology (see Table 2). While its Societal Acceptance Modelling is used as an Assessment Methodology, it is used to assess and long-term model the HRI (refer to Table 4). As shown in Table 2, using EOC method is that it provides a quantifiable metric that can be used as a basis of comparison between different user groups, interaction spaces and between different robots [131]. Subjects would have unstructured interactions with the robot and asked to state when they have determined the robot’s type and role. The subjects will be asked to think aloud during the experiment [129,131]. 4.9. Psychological and therapeutic models for HRI, based on Proximity Theory in Psychology and Cognitive Science By combining the knowledge from Proximity Theory in Psychology as well as Cognitive Science and the like, we can assess human–robot communication and then, evaluate and long-term model the HRI. For instance, Kanda et al. utilize the robots’ body properties for facilitating the interaction with humans [72,82,86] and cause people to unconsciously behave as if they were communicating with humans [82,85]. (Refer to Figs. 11d and 11e for interactive behaviours of the humanoid robot [82].) When we review another social model of proximity control for information-presenting robots, Yamaoka et al. in 2010 did establish a good model for information-presenting robots to appropriately adjust their position. The experimental results verified the effectiveness of the model and showed that an information-presenting robot using their model presents an object better than by using simpler models. The primary evaluation methodology used by them is also the subjective selfassessment or self-report [181]. Again, the assessment methodologies under this evaluation methodology include observations and participants’ impressions toward the robot. Kanda et al. in 2008 compared two humanoid robots, ASIMO [139] and Robovie [82], and a human [86]. They found that not only the impressions, but also the attributions such as humanity, affected the participants’ non-verbal behaviours (refer to Tables 2 and 3). There were no differences found in their verbal behaviours. In this research, Kanda et al. [86] formulated a statement or formula to model human behaviours to robots or humans as: Non-verbal behaviours = f (Impressions, Attribution). For instance, the distance during talking and walking during the HRI show similar tendencies to familiarity, and this is consistent with Proximity Theory in Psychology, as proposed by Hall in 1990 [50,86]. The attribution includes ‘whether it is respected as the conversation partner or not’ [86]. In terms of Therapeutic and Psychological Modelling, Kozima et al. in 2005 supported in their research by proposing a possible use of interactive robots in the remedial practice for children with Autism [97]. The assessment methodology used is based on a small creature-like robot, Keepon, which was carefully designed to get autistic and non-autistic children involved in playful interaction (see Fig. 10a) [97]. This research is further supported by their work later in 2009 [96]. They undertook a similar study to investigate the interaction between toddlers and Keepon, a small robot designed to interact with children through non-verbal behaviours [96] (see Figs. 15a and 15b). In 2005, Kozima et al. observed how autistic children (2–4 years old) interacted with Keepon. Each child showed a different style and a different unfolding of interaction over time, which told us a ‘‘story’’ of his or her personality and developmental profile, which would not be explained completely by a diagnostic label like ‘‘autism’’ [97]. Hence, their research further confirms the usefulness of Psychological and Therapeutic modelling approaches. 4.10. Integrated model of personality and affect (TAME), ethological and emotional models for HRI Tapus and Mataric’ analyse how the varying minor characteristics of the robot’s personality gives impacts to the user’s efficiency during HRI and whether the robot is able to converge to a set of characteristics that are in consensus with the user’s preferences (see Fig. 10a) [167]. This is because people have stronger empathic emotions and reactions when the interaction episodes are associated with others with whom they have a social relationship (such as with friends or with family members) or a common background (such as a person who lived through a similar experience) [150,167]. Arkin et al. in 2003 [1], as well as Moshkina and Arkin in 2005 [113], have respectively used Ethological and Emotional Models [1] as well as the integrated model of personality and affect (TAME, i.e. Traits, Attitudes, Moods and Emotions) [113] to assess and long-term

328


model the HRI (see Table 3 for detailed illustrations on the comparisons and contrasts). These models have presented design of framework which increases the ease and pleasantness of HRI [1,113]. So, these are good assessment methodologies implemented to model the HRI. As shown in Fig. 10b, the personality of the robot is expressed through the extroversion–introversion personality trait, and this is important for the user–robot personality matching [167]. The robot’s behaviour usually has a range from non-social to social, low activity to high activity, in order to express the extroversion (i.e. challenging) or the introversion (i.e. nurturing) therapy styles [82,167]. (Refer to Figs. 11a–11e). Although various learning approaches for HRI were proposed by past researchers, such as by Tapus and Mataric in 2006, Breazeal and Scassellati in 2003, none of them includes the user’s profile, preferences and personality trait. Tapus’ and Mataric’ proposed work [167] is to create assistive social robots which are capable of monitoring and enhancing physical therapy (see Fig. 6). They proposed a methodology for evaluating a reinforcement-learning-based approach to robot’s behaviour adaptation. This learning approach would incrementally adapt the robot’s behaviour to better model the user’s personality and needs, and so, improve users’ task performance [167]. Bartneck et al. in 2007 [4] have shown that robot’s perception is culturally dependent on a study comparing the measured attitudes for participants from different nationalities. Results indicated that the Japanese are concerned about the impact that robots might have on society and that they are particularly concerned with the emotional aspects of interacting with robots [4,55]. Further to these studies done by Bartneck et al. in 2008, to emphasize the need for standardized measurement tools for HRI, they had shown their abilities to compare the results done from different previous studies [2,3]. Thereafter, Bartneck et al. in 2009 investigated the influence of two different embodiments, i.e. Robovie II robot and iCat robot (see Figs. 12a and 12b), on how robots are perceived in terms of Animacy and Perceived Intelligence [2]. Animacy and Perceived Intelligence are two of the components of Godspeed Five Key Concepts modelling [3]. To support this modelling approach, Gonsior et al. in 2011 did researches on improving aspects of empathy and subjective performance for HRI through mirroring facial expressions [47]. They conducted experiments so as to evaluate the long-term modelling of ‘Five Key Concepts in HRI’ as proposed by Bartneck et al. in 2008 [3]. User acceptance is evaluated according to the analytical measures of Heerink in 2009 [56]. A measure of empathy and subjective task performance experienced by the user is assessed to reveal any possible correlation. They conducted a study which the participants were asked to rate empathy and task performance of the robot [47]. This again supports the Godspeed Five Key Concepts Modelling as the main assessment. In their previous experiment on a route-guidance situation [76], Kanda et al. observed that human participants used different words to humans and Robovie (for example, giving different landmarks to Robovie) [76,82,86]. Kanda et al. conducted a comparison of impressions based on the factor scores, and then followed the method reported by them in 2001 [81]. They conducted factor analysis on the Semantic Differential ratings, and adopted a solution that consists of four factors. These four factors were interpreted by referring to factor loadings, i.e. Familiarity, Novelty, Safety, and Activity factors [86]. However, the limitations of this research are that it does not ensure whether the research findings can be applied to all other humanoid robots, besides the two existing ones, i.e. Robovie [82] and ASIMO [139]. Hence, the general application of this research is limited. In addition, this experiment only involves a situation reflecting first-time conversation [86]. It seems that Novelty had larger effect on the non-verbal behaviours that did the other factors [80,86]. This further supports the research done by Kanda et al., in 2004 on interactive robots based on their non-verbal behaviours [80] (refer to Tables 2 and 3 for further details). 4.11. Temporal awareness modelling and timing modelling for assessing and long-term modelling the HRI According to Shiwa et al. in 2008 and 2009 [152,153], a robot cannot always respond in such a short time as one or two seconds. So, what should a robot do if it cannot respond quickly enough? Ref. [153] some researchers have tried to design specific modelling approaches or optimization paradigm or HRI models which are of temporal awareness

Fig.10(b). Personality Model of the user and its empathy level [166] Fig. 10b. Human–Robot Interaction information processing using user’s Personality Model and his empathy level (Tapus and Mataric’ [167]).

329


(a)

(b)

(c)

Fig. 11a–c. (a) Robovie – a robot with sufficient physical expression ability (Kanda et al. [82]). (b) A scene of joint attention, i.e. eye contact and pointing to share the attention (Kanda et al. [82]). (c) Development of a situated module with communicative units (Kanda et al. [82]) All the above can be used as the Task Performance Metrics of the HRI, serving as one of the Primary Evaluation Methodologies for HRI.

(d)

(e)

Figs. 11d and e. (d) Humanoid robot’s interactive behaviours [82]. (e) Attached markers (left) obtained 3-D animated images (right) [82].

Fig. 12a. iCat robot (left) (Bartneck et al. [2,86] and Saerbeck et al. [137]) and the Robovie II (right) robot (Bartneck et al. [2,82,86]).

[17–19,43,44,82,83,107,108,126]. They assessed on HRI based on specific task performance metrics as parts of the primary evaluation methodologies on HRI. These models and modelling approaches are usually used when timing and the operator’s role for tele-operating the multi-robot system are of mission critical [17,18,108,119,126,178]. Mutlu et al. in 2013 used specific coordination mechanisms to enhance HRI, these models worked well with the Human Robot Team (HRT) models involving multi-robot systems [119]. In years 2012 and 2013, Mora et al. used a tele-operation approach on mobile social robots, incorporating automatic gaze control and three-dimensional spatial visualization, a much more sophisticated evaluation methodology has been explored [18,108,109]. While in both the years 2012 and 2013, Melin et al. used optimal design of type-2 and type-1 fuzzy tracking controllers for controlling autonomous mobile robots under perturbed torques [17,18,108] (see Table 4). In 2013, Melendez and Castillo stressed about using evolutionary optimization of the fuzzy integrator in a navigation system for a mobile robot [107]. Till this year, i.e. 2014, Pateraki et al. used visual estimation of pointed

330


Fig. 12b. Robovie R-1, the robotic platform that Mutlu et al. used in the evaluation of the gaze mechanisms studied in 2012 (Mutlu et al. [117]).

(c)

(d)

Fig. 12c and d. (c) Scene of eye contact (Kanda et al. [82]). (d) Scene of synchronized body movements (Kanda et al. [82]).

targets for robot guidance, the temporal awareness is done via fusion of face pose and hand orientation [126]. We will discuss the trends of this technology advancement in recommending our proposed hybrid of HRI methodologies in Section 6! 5. Evaluation methodologies (primary and non-primary) that have been carried out to evaluate and long-term model the HRI 5.1. Convergent validity for the primary evaluation methodologies for long-term modelling of HRI According to the key researches done by Bethel et al. in 2007, and then Bethel with Murphy in 2009, there are four primary methods of evaluation used for human studies in HRI [5,7,8]. For the fourth evaluation methodology, (i.e. task performance metrics measurements), it can be compared with the classical subjective evaluation results. This has been applied by Kanda et al. since 2004. It is the comparison method of the movement interactions during HRI [54]. These primary evaluation methodologies include: (1) (2) (3) (4)

Self-Assessments Subjective Evaluation [5,8]. Behavioural measurements [5,8]. Psycho-physiological measures [5,7,8]. Task performance metrics [5,8,14,122,158]; (this may include comparisons of the measurements on the movement interactions during humanoid-robot and human interaction [82,83]) see Figs. 11d and 11e.

The most common methods utilized in HRI studies and research so far are the self-assessment and behavioural measures [5]. This is probably because there has been quite limited research done in the use of psycho-physiological measures and task performance metrics. Each method has its own advantages and disadvantages. However, according to the researches done by Kidd and Breazeal in 2005, and Bethel et al., in 2007, some of these disadvantages can be overcome by using more than one method or methodology of evaluation [5,8]. 5.1.1. Self-assessment subjective evaluation methodologies Accordingly, the first of the listing above, i.e. the use of self-assessments is one of the most commonly used primary evaluation methodology in HRI studies. Self-assessment measures include paper or computerized psychometric measures, questionnaires, and/or surveys. For this evaluation method, participants provide a personal assessment of how they felt or their motivations related to an object, situation, or interactions [5]. However, the weaknesses of this method are that there are


331

often problems with validity and corroboration although self-assessments provide valuable information. These can be shown in some of the researches done in the same year and thereafter [2,150]. In a deeper illustration of this weakness is that, participants may not answer the questions based on how their feeling or perception is at that time, but rather very unrealistically, i.e. based on how they feel others would answer the questions or in an imaginative way they think what the researcher wants them to answer [5]. So, more than one methodology of evaluation is always required [5,7,8]. Another weakness of self-assessment measures is that observers are not able to immediately and directly corroborate the information provided by the participants. While on the other hand, participants may not be in touch with what they feel about the object, situation, and/or interaction, and therefore they may not report their true feelings. The participants’ responses could be influenced by their mood and state of mind on the day of study [5,36]. All these can cause inaccuracies of this assessment methodology. According to Bethel’s and Murphy’s research work in 2009, the design of a quality research study, especially for the use in HRI applications, is a major challenge, when producing results that are verifiable, reliable and reproducible is a must. This is because the use of a single method of measurement is not sufficient to interpret accurately the responses of participants to a robot with which they are interacting [5]. Moreover, in addition to that, Steinfeld et al. (2006) describe the need for the development of common metrics as an open research issue in HRI by discussing an approach of developing common metrics for HRI [158]. However, the weakness of this type of approach is that it is oriented more toward an engineering perspective, but does not completely address the social interaction perspective. Both the engineering and social interaction perspectives require further investigation in order to develop common metrics and methods of evaluation [5,36,75]. For these reasons, it is important to perform additional types of measurements, e.g. behavioural, task performance, and/or psycho-physiological measures, in order to add another dimension of understanding the HRI studies [5,8]. 5.1.2. Behavioural measurements For the second evaluation methodology, i.e. behavioural measurement is considered as the second most common method of evaluation in HRI studies. This method is sometimes included along with the psycho-physiological evaluations, and participants’ self-assessment responses for obtaining convergent validity [5]. John and Christensen (2004) define observation as ‘the watching of behavioural patterns of people in certain situations to obtain information about the phenomenon of interest.’ This is because the ‘Hawthorne effect’ is a concern with observational studies, i.e. it is a phenomenon in which participants know that they are being observed, and this impacts their behaviours [36,75]. To further support this measurement, Kanda et al. developed a human friendship estimation model for communication robots in 2008 [87], while Heerink et al. built an Almere model (see Table 3) for measuring the acceptance of assistive social agent technology by older adults in 2010 [62] (see Figs. 13a–13c). They all used the participants’ self-assessment responses, i.e. questionnaires for obtaining convergent validity for the evaluation models used in behavioural measurements for HRI [57,62,87]. Again, this supports the concept that evaluation methodologies should work with assessment methodologies for achieving a better HRI measurement and modelling. 5.1.3. Psycho-physiological measurements For the above weaknesses, especially due to the ‘Hawthorne effect’, the third evaluation methodology, i.e. the psychophysiological measures, can assist for obtaining a better understanding of participants’ underlying responses at the time of observations [5]. The benefit of using the behavioural measures is that this method is less biased. This is because researchers are able to record the actual behaviours of participants, and do not have to rely on participants to report their intended behaviours or preferences [5,7,8,36]. In addition, video observations are often recorded in order to be later coded for visual and auditory information using two or more independent assessors [5,14]. This method of evaluation, i.e. using the psychophysiological measures, is gaining more popularity in the HRI studies because, as mentioned above, it is a relatively fair approach. This is because participants cannot consciously manipulate the activities of their autonomic nervous system [5,7,71,92,99,103,128,130]. Psycho-physiological measures offer a non-invasive method that can be used to determine the stress levels and reactions of participants interacting with the technology [5,71,99,103,128,130]. However, on the other hand, one of the weaknesses of using psycho-physiological measurements is that it can complicate the process because the results are not always straightforward and confounds can lead to misinterpretation of data. There is a tendency to attribute more meaning to results because of the tangible nature of the recordings. Information needs to be obtained from participants prior to beginning a study in order to help reduce these confounds, such as health information and state of mind. Multiple physiological signals should be used to find correlations in the results [5,7,8]. 5.1.4. Task performance metrics For the fourth evaluation methodology, i.e. the use of task performance metrics, is evolving and becoming more common in HRI studies, especially where teams or groups are being evaluated and/or more than one person is interacting with one or more robots [5,14,56,118,158]. This method has also been used by a number of researches done by Kanda et al., such as in the researches of analyzing of the humanoid appearances in HRI [86]. (Sub method is usually incorporated in this task performance metrics measurement to augment this Primary Evaluation – comparisons of the movement interactions for humanoid robot–human interactions with traditional subjective evaluations.)

332


Below is a review of this type of evaluation methodology implemented by a few past researchers. Kanda et al. in 2004 [82] measure the body movement interaction between a humanoid robot (such as Robovie – see Fig. 11a) and humans. Then, they compare the results with traditional subjective evaluation results. Through the evaluation experiments, they argue about the performance of the developed interactive humanoid robots and provide perspectives for the new analytical method for HRI. This research is further supported by researches done by Kanda et al. in 2008 and 2009 [82,86] as well as Mutlu et al. in 2012 [117] (see Fig. 12b) and Frank et al. in 2014 [39]. In terms of correlations between body movements and subjective impressions, Kanda et al. employed an optical motion- capturing system to measure the body movements of the robot (see Fig. 12c and 12d). Comparison between the body movements and the subjective evaluations indicates the meaningful correlation. Well-coordinated behaviours, such as eye contact and synchronized arm movements are really important [82] (see Figs. 12c and 12d). In 2014, i.e. this year research update, Cooney et al. has demonstrated this type of primary evaluation methodology to design robots for well-being [25,26]. This is based on visual scenes of affectionate play with a small humanoid robot [26], and also through designing very enjoyable motion-based play interactions with a small humanoid robot [25]. In the same vein in this year 2014, Frank et al. emphasized on using curiosity driven reinforcement learning for motion planning on humanoids [39] – task performance metrics are important in terms of primary evaluation basis. Kanda et al. performed an experiment to evaluate the developed humanoid robot and analyzed the interaction between robots and humans. In the experiment, the humans behave as if they were interacting with a human. They kept eye contact with the robot and imitated the gestures of the robot (refer to Figs. 11d and 11e and 12c for illustrations). These entrainments of body movements indicate the high performance of the developed robot during the HRI [82]. The Entrainment Scoring techniques, are used as the non-primary evaluation methodologies for HRI. The robot’s task performance metrics during the HRI are the primary evaluation methodologies for HRI. Positive correlations between cooperative body movements and subjective evaluations are discovered by Kanda et al. [82,83]. As shown in Figs. 11d and e and 12c and d, comparisons of the measurement on the movement interactions during the humanoid robot-and-human interaction with the subjective evaluation results have been conducted profoundly [2,82,117]. These task performance metrics are designed to measure how well a person or a team performs or completes a task or tasks. This is essential in HRI studies and should be included with other methods of evaluation, such as behavioural and/or selfassessment methodologies [5,14]. Bethel and Murphy presented the research outcome in their paper (2009) by utilizing the above four primary evaluation methodologies as described. This is done so that the convergent validity may be obtained to determine the effectiveness of non-facial and non-verbal affective expressions for naturalistic HRI social interaction. Multiple self-assessments were used during HRI [5,118,145,158]. In the same research vein, Burke et al. [14] took a systems approach for measuring task performance metrics. As mentioned above, the evaluation methodology is a measurement tool, incorporated together with subjective evaluation results from participants [117,141]. The behaviours of robots do not violate their Type or Role Classifications, and thus are generally well accepted [131,160,173] (see Figs. 13a–c and 14). 5.2. Non-primary evaluation methodologies for efficient and thorough evaluation criteria for HRI The non-primary evaluation methodologies may sometimes be used as supplementary to indirectly evaluate HRI. It is important to use more than one evaluation methodology in a comprehensive study to gain a better understanding of Human–Robot Interaction. Within a single methodology of evaluation, there are usually more than one assessment measurement to be utilized [2,5,82,117,134,138].

Fig. 13a–c. (a) Seal robot, named Paro, which has a behaviour generation system that consists of proactive and reactive processes. These two layers generate three kinds of behaviours, i.e. proactive, reactive, and physiological behaviours (Wada et al. [173]). (b) Interaction between elderly people and Paro (Wada et al. [173]). (c) Results of Average Scores of a Question Item ‘‘Vigorous’’ of Elderly People for 6 weeks’ period [173].


333

Fig. 14. The Robotceptionist (Riek and Robinson in 2008 [131]). As the Ease of Classification (EOC) measurement, take note of the flowers, business cards, and memorabilia surrounding the desk, as well as the office attire that the robot is dressed in [131]. Using EOC formula score is a good non-primary evaluation methodology for HRI.

5.2.1. Non-primary evaluation methodologies incorporated under the four primary evaluation methodologies As mentioned in Section 5.1 above, we investigate how many or to what extent the assessment methodologies are needed to be incorporated in each of the major four Primary Evaluation Methodologies stated above. (1) For the first primary evaluation methodology, i.e. self-assessment methods of evaluation, a lot of researchers use more than one assessment methods within just this methodology of evaluation. The assessment methodologies include using different types of learning approaches, social models, passive-social media, other similar media, open platform or the like [5,8,47,51–53] (refer to Fig. 16a); (2) for the second primary evaluation methodology, i.e. behavioural studies for HRI [173] (see Figs. 13a–13c), observations should be obtained from more than one angle or perspectives [5,8]; (3) for the third primary evaluation methodology, i.e. psycho-physiological studies, more than one signal should be obtained for validity and correlation [5,7,8]; and (4) for the fourth primary evaluation methodology, i.e. task performance evaluation criteria or task performance metrics, it should be measured in more than just one way [5,8]. This is because task performance metrics involve measurements from a lot of measurement tactics that differ in different kinds of HRI. For the sub-method incorporated together with this fourth evaluation methodology, i.e. comparisons of body movements with classical subjective evaluation results are also conducted so that for more efficient and thorough evaluations for HRI can be adopted. 5.2.2. Ease of Classification (EOC) as non-primary evaluation methodologies for the societal acceptance of robots Riek and Robinson in 2008 [131] use an Ease of Classification (EOC) score as a means of measuring the societal acceptance of robots. As mentioned above, this can be a type of non-primary evaluation methodology for HRI. Using the EOC score formula as a means of measuring the societal acceptance for robots has a few quite profound advantages (see Tables 2 and 4 for the strengths and weaknesses of using EOC and its Societal Acceptance Modelling approaches). This is because, upon the very first encountering towards a robot within its intended physical space, it should be immediately apparent to the users what general purpose the robot is intended to serve, i.e. its type. In other words, to indirectly evaluate HRI using EOC, the robot’s physical appearance, movement, gait, speech, gesture, gaze, or stature can reflect exactly this purpose [131]. Preceding this research conducted by Riek and Robinson in 2008, Gockley et al. [45] in 2005 showed the roboceptionist as a measurement for the EOC evaluation methodologies. For instance, when one encounters Tank the Roboceptionist (see Fig. 14) at Carnegie Mellon, it is very easy to classify its type as receptionist. From this figure, we can see that Tank is located near the entrance to a building inside a wooden booth. This robot (as shown in this figure) is unlikely to be mistaken for anything because its design and physical placement clearly reflects its type and purpose [45,131]. To further support this non-primary evaluation methodology towards HRI, Sung et al. [163] in 2008 show examples in our daily lives such as people who interact with personal robots in the home will often dress them in costumes. Perhaps, this is a means to help other family members and visitors to the home to readily classify the robot as non-threatening [163]. So, using EOC evaluation methodologies can be a good way to indirectly using users to evaluate the HRI based on how easy they think the robot can be classified [45,46,131,163]. Riek and Robinson proposed the EOC Score formula to calculate the EOC score, forming the Classification Ease to the HRI researcher’s toolset [131]. 5.2.3. Psychological human–humanoid robot interactions as the non-primary evaluation methodologies for estimation of momentary evaluation score for HRI Kanda et al. in 2004 [82] conducted the momentary evaluation score, i.e. entrainment score, through comparisons of body movements with traditional subjective evaluations – this is for indirect evaluation of HRI. This entrainment score serves as a non-primary evaluation methodology, while the different parameters measured can be the task performance metrics serving as a primary evaluation methodology for HRI. So, for this research, there is a combination of primary and non-primary evaluation methodologies (refer to Tables 1 and 2), but the assessment methodologies applied during HRI are not very prominent. The humanoid robot, Robovie (refer to Figs. 11a, 12a, 12c and 12d), is used as a test-bed for studying embodied communication [82] (refer to Table 4). The non-primary evaluation methodologies involve Kanda et al.’s [83] constructive

334


(a)

(b)

Fig. 15a and b. (a) Keapon, the creature-like robot, performing eye-contact and joint attention with the human interactant (Kozima et al. in 2005 [97] and in 2009 [96]) – refer to Table 4 for the uniqueness of Keepon. (b) Keapon’s external and internal structure and its deformable body made of silicone rubber (Kozima et al. in 2005 [97]). Keepon’s structure: its simple appearance and marionette-like mechanism (left), which drives the deformable body (right) (Kozima et al. in 2009 [96]) – refer to Table 4 for the uniqueness of Keepon.

(a)

(b)

Fig. 16a and b. Non-social condition (left) and social condition (right). Non-primary evaluation of HRI by observing and recording the different behaviours of each station user, and correlating each of them with the questionnaires given to the station users (Hayashi et al. in 2007 [53]). (b) Outline of a multi-robot communication system (Hayashi et al. respectively in 2005 [52], 2007 [53] and 2008 [51]).

approach, which is to continue implementing behaviours of the interactive humanoid robots until humans think that the robot has an animated and lifelike existence that is beyond that of a simple automatic machine. 5.2.4. Formulations as non-primary evaluation methodologies for the non-verbal behaviours of robots Kanda et al. [86], in 2008, performed analysis of humanoid appearances in HRI that was also through ‘non-verbal behaviours formulations’ on the robot. This type of non-primary evaluation methodology is conducted and supported by other similar researches [50,76,80]. Kozima et al. in 2005 [97] and 2009 [96] used Keepon, a creature-like robot in real-world setting (see Fig. 15a and b above), to interact with autistic children through non-verbal behaviours and long-term HRI is modeled through psychological and therapeutic modelling approaches (refer to Table 4). 5.2.5. Using robots to serve as passive-social media or using open exploring platform for exploring HRI Researches in Human–Computer Interaction (HCI) have highlighted the importance of robots as user interface media [51,52], or as round display modules that can extend on open exploring robot platform [73,74]. They believe that humanoid robot(s) will be, used as interface media especially of passive social, particularly by showing conversation among multiple robots [51,52] (see Fig. 16b) or as Glowbots creating interesting patterns to attract users [73,74] (see Fig. 7a and b, by Jacobsson et al.). To support this, Kanda et al. proved that users can understand a robot’s speech more easily, and more actively respond to it, after observing the conversation between two robots or among multiple robots [53,84] (see Fig. 16a left figure and Fig. 16b right figure). Preceding this research, Hayashi et al. have shown that using non-primary evaluation methodologies of correlating through hypothesizing methods gives significant findings, which was the most effective way of attracting people’s interest during HRI [51–53] (refer to Table 2). As mentioned in Section 4.4 above, Jacobsson et al. showed that users can indirectly evaluate the HRI through observations on the Glowbots’ demonstrations on the robot platforms [73,74]. Please view all the Tables 1–8. 6. Contributions of this review paper 6.1. Contributions in providing our insights and vision for future HRI assessment and evaluation methodologies After looking extensively at all the major HRI assessment and evaluation methodologies mainly from the year 2000 till 2014, and from all the 4 summarized tables on the Primary and Non-Primary Evaluation Methodologies as well as the Assessment Methodologies stated, from the trends of technology advancement over the years, Table 6 below clearly states


335

Table 5 Our recommended questions for future research in Assessment and Evaluation Methodologies for HRI.

How to further refine the learning approaches applied for the Assessment Methodologies for long-term modelling of Human–Robot Interaction (HRI)? How to further improve each of the four major primary Evaluation Methodologies reviewed in this paper for the betterment in long-term modelling of the Human–Robot Interaction (HRI)? How to further improve each of the major non-primary Evaluation Methodologies reviewed in this paper for the betterment in long-term modelling of the Human–Robot Interaction (HRI)? How to develop a better social model or social assistive model in the HRI so that robots can learn from their past mistakes and keep improving so as to achieve a higher evaluation score for Human–Robot Interaction (HRI)? Which combination of Assessment and Evaluation Methodologies for HRI is the most suitable for which type of robot to interact with humans? How to develop a better learning approach in the Assessment Methodologies so as to promote Human–Humanoid Robot Interaction? Which combination of Assessment and Evaluation Methodologies for HRI is the most suitable to be applied in which type of Robotic Environment? How can a Human–Robot Interaction (HRI) be evaluated successfully by a combination of Evaluation Methodologies so that the primary and nonprimary evaluations can ‘work’ synergistically? For interactive Human–Robot Communication, which kind of learning approaches or monitoring control in the Assessment Methodologies should be adopted by the robot so that a better evaluation score for HRI can be achieved? How can the interactive social learning model adopted by the robot be used as an emulator to promote a better long-term modelling of HRI, such as a better vicarious HRI? If so, which combinations of assessment and evaluation methodologies should be used for assessing and evaluating HRI in order to promote a better long-term modelling of HRI?

and illustrates our new insights, our own vision, and inspirations for future HRI assessment and evaluation methodologies to be adopted. The inspirations for future improvements from current research work done on assessing and evaluating HRI are that we proposed possible hybrids of HRI methodologies or combinations of HRI methodologies to provide solutions for the limitations of the HRI assessments and evaluations surveyed so far. In Table 6, we summarised the advantages of our new insights and our inspired HRI methodologies that we have proposed so that future improvements can be done!! We have thoroughly reviewed, discussed and analysed extensively almost all the assessment and evaluation methodologies for modelling HRI, especially for long-term modelling of HRI. Although a large amount of research has been done in assessment and evaluation methodologies for HRI in order to have better modelling approaches for HRI, many issues still remain open. However, in our vision for future, our recommended Types I and II combinations of methodologies (as stated in Tables 7 and 8) for assessing and evaluating HRI, can help to achieve the following: (1) Contributions in providing the best suits for ongoing improvement learning and modelling approaches for HRI: The characteristic feature for each combination of assessment and evaluation methodologies can be achieved by using Recommended Type I as stated in Tables 7 and 8 below. This is because HRI is getting more and more complex as well as advanced. Our recommended Type I combination of HRI methodologies stated below is more appropriate for the elderly people as it increases the ease and pleasantness of HRI. It gives more social impacts to societies by achieving more integrated shared humans’ intentions. (2) Contributions in certain Robotic environment which is less or least biased for ongoing improvement and modelling, i.e. assessment, as well as for discrete or final judgements, i.e. evaluation: Recommended Type II combination of methodologies (as stated in Tables 7 and 8 below) ensures a good robotic environment so that the facilities, equipments and devices can be fully utilized. The long-term modelling effects of HRI can be enhanced as well as assessed and evaluated properly, involving even greater numbers than a multiple of robots and humans in the interaction, for better multi-tasking, entertainment and presentation purposes. (3) Contributions in providing Interactive Social Learning and Modelling Approaches: Recommended Type I combination or hybrid of methodologies (as stated in Tables 7 and 8 below) ensures good intrinsic and extrinsic learning models for the assistive social robots, especially for the humanoid robots. A better learning model or reinforcement-based learning model for assistive social robots can assist more for the elderly. (4) Contributions in rendering HRI Model and other modelling approaches such as the Empathic Model mainly to the Societies and Robotics, AI or IT Industries: Recommended Types I and II combination or hybrid of methodologies (as stated in Tables 7 and 8 below) can let assistive social robots adopt much better HRI model and HRT Modelling as well as assistive social modelling approaches during the HRI. Keeping in view of all the future research directions as stated above, we hope that this review paper has given good insights and thorough summarized review for all or almost all the HRI assessment and evaluation methodologies done by the past researches on assistive social robots during the HRI. All these assessment and evaluation methodologies which are reviewed in this paper are also for long-term modelling of the HRI.

6.2. Contributions of our recommended types in terms of Social and Industrial Impacts What are the social and industrial impacts of our reviewing work done so far? Well, after reviewing the HRI assessment and evaluation methodologies mainly from the year 2000 till 2014, in this subsection, based on the 6 summarized tables

336

Table 6 New insights, directives and inspirations for improvements of HRI assessment and evaluation methodologies. Difficult issues during HRI assessments and evaluations

In HRI Evaluation

In HRI Assessment

(Assessment Methodologies) (1) Human-control of Multiple Robots; (2) Tele-operation of Multiple Social Robots Model Plus (Primary Evaluation) Task Performance Metrics

(1) Evaluation is mostly limited on questionnaires; (2) Task Performance Metrics suitable for humanoid robots only; (3) Not suitable for one-to-one HRI

(1) Parameters for the models only in a specific context; (2) Models only cover HRI on limited aspects; (3) Assessment does not model random errors of automation. So, less accurate; (4) Timing is of mission critical

(Assessment Methodology) (1) UTAUT Model; (2) iCat robot (as a test-bed for social intelligence)

(1) Questionnaires used are specific to certain contexts only – not standardised enough; (2) Questionnaire method is subjective, and hence, biased Evaluation methodology is not suitable for non-humanoid robots

UTAUT model applied is a modified version, researchers can only draw tentative conclusions from this measurement

(Assessment Method) Societal Acceptance or Technology Acceptance Modelling; plus (Non-Primary Evaluation) EaseOf-Classification (EOC) formula

Users may feel reluctant to classify the robots

Bias may be incurred (as no objective measurements in HRI assessments)

(Assessment Methods) (1) HRI model with HCI and CSCW incorporated (2) Open exploring robot platform, i.e. see Puck and e-Puck with Glowbots

Evaluation methodologies are subjective, hence incurs bias

Assessment modelling may not be thorough enough as not all concepts are taken in account

(Assessment Methodology) Different Gaze Model(s)

Evaluating gaze mechanisms is usually tedious and timeconsuming

Assessment is not suitable for nonhumanoid robots

(Assessment Methodology) Psychological and Therapeutic Modelling; Plus (Primary Evaluation) Ethnographic

Evaluation methodologies are specific for autism and so, limited application

Assessment modelling is not thorough enough as not all concepts are taken in account

(Assessment Methodology) Temporal Awareness Model or Timing Model; plus (Primary Evaluation) Task Performance Metrics

(1) Technical difficulties may incur problems during timing control; (2) Problems may occur during the teleoperation of interface design

Our Proposed Solutions to eliminate the Limitations (In HRI Assessments and Evaluations) (Contributions to the Society)

Our Proposed Insights – (In HRI Assessments and Evaluations)

(1) Combine the system with advanced HCI and CSCW techniques and well-operated Human–Robot Team (HRT) modelling so that it is also suitable for non-humanoid robots as well as one-to-one HRI, even if real-world applications. Wider scope of application too. (2) Well-adjusted and autonomous Task Performance Metrics that have excellent timing control and almost error free in real-world deployment Implement more advanced HCI and CSCW to ensure wider social intelligent acceptance and application of the UTAUT model

A combination of more advanced and automated HCI or CSCW and/or XMLbased visualization system to the HRT modelling with well-adjusted Task Performance Metrics

(1) Combine with well-operated Human– Robot Team (HRT) model or other multirobot models so that the timing control can be well-adjusted; (2) Incorporate HCI/ CSCW into the Human–Robot Interaction (HRI) model so that the evaluation methodologies can be suitable for nonhumanoid robots too Combine this hybrid of methodologies with psycho-physiological measurements so that objective measurement on HRI is involved. Bias can somehow be eliminated even if users feel reluctant to classify the robots Incorporate Technology Acceptance Modelling (TAM) or Godspeed Five Key Concepts Modelling as well as psychophysiological measurements into the HRI assessments to eliminate bias and incompleteness in assessments (1) Incorporate HCI/CSCW into Gaze model so that the evaluation methodologies can be suitable for non-humanoid robots too; (2) HCI/CSCW can be tele-operated, i.e. every control is automated and really fast Incorporate Technology Acceptance Modelling (TAM) or Godspeed Five Key Concepts Modelling to the Psychological and Therapeutic Modelling plus more

A combination of Temporal Awareness Model with well-adjusted Task Performance Metrics (suitable for both humanoid and non-humanoid robots)

(Contributions to the Robotics Industry) (for improvements on HRI Assessments and Evaluations)

A combination of more advanced and automated HCI or CSCW and XMLbased visualization system to the UTAUT Model

A combination of Technology Acceptance Modelling (TAM)/UTAUT modelling with EOC and psychophysiological measurements

A combination of TAM or Godspeed Five Key Concepts Modelling with advanced HCI and CSCW networking and psycho-physiological measurements A combination of more advanced and automated HCI/CSCW and/or XMLbased visualization system to the Gaze Model(s) A combination of TAM/UTAUT model or Godspeed Five Key Concepts with Psychological and Therapeutic Modelling can be incorporated with


Existing Assessment and Evaluation Methodologies currently adopted or have been adopted (surveyed mainly from 2000 till 2014)

Observations (Assessment Methodologies) (1) Behavioural Adaptation Model; (2) User Personality Matching Model or User–Robot Personality Matching Model

(1) models are subjective, and so, biased; (2) specific only for therapeutic purposes; (3) models may take a long time to develop

(Assessment Methods) (1) HRI model with HCI and/or CSCW incorporated; (2) robots as passive-social media

Evaluation is of narrower application, i.e. only on the user interfaces

Assessment is limited on robots while acting as passive social media

(Assessment Methodology) Godspeed Five Key Concepts Modelling

Evaluation methodology of using questionnaires is subjective, and so, biased

Modelling is more subjective, less objective

standardized behavioural observations (1) Emphasize more on psychophysiological measurements as primary evaluation method in order to eliminate bias; (2) Incorporate Technology Acceptance Modelling (TAM) or Godspeed Five Key Concepts Modelling to ensure more standardized HRI assessment modelling and evaluation methodologies Implement more advanced HCI and CSCW to ensure wider application on HRI modelling other than just on user interfaces, and ensure the HRI assessment is beyond robots acting as passive-social media Incorporate psycho-physiological measurements into the Godspeed Five Key Concepts Modelling to introduce objective measurement, and hence, to eliminate bias

behavioural observations A combination of TAM or Godspeed Five Key Concepts Modelling with Behavioural Adaptation or User Personality Matching Model with emphasized psycho-physiological measurements

A combination of more advanced HCI and CSCW and/or XML-based visualization system to HRI modelling

A combination of Godspeed Five Key Concepts Modelling with psychophysiological measurements as emphasis


Mostly subjective primary evaluation methodologies are used, and hence, biased

337

338


Table 7 Our strongly recommended HRI assessment and evaluation methodologies. Our Strongly Recommended Hybrids of HRI Assessment and Evaluation Methodologies Recommendation Type I A hybrid of the following:(Assessment Methodologies) (1) UTAUT Model (2) Godspeed Five Key Concepts Modelling (3) HRI Model with HCI and CSCW incorporated (4) Model of integrated intentions, e.g. Haptic channel (Evaluation Methodologies) (1) Self-Assessments Subjective Evaluation (2) Psycho-Behavioural measurements Recommendation Type II A hybrid of the following:(Assessment Methodologies) (1) UTAUT Model (2) Timing Model (3) Human Robot Team (HRT) Model (4) Fuzzy Integrated Model (Evaluation Methodology) Task Performance Metrics

Reasons for recommending this combination of HRI Evaluation and Assessment Methodologies – Strengths of each Recommended Type (Contributions to the Societies and Industries)

Contributions to the Societies and to Robotics, AI and IT Industries

This recommended type has more Social Impact to HRI This combination of methodologies is thorough, robust, and with advanced Human–Computer Interaction (HCI) and Computer Supported Cooperative Working (CSCW) on user interfaces, it ensures wider social intelligent acceptance, especially in the elderly population. This approach is suitable for general population as well

Used for the wellness, counselling and companionship purposes, especially for the elderly people, autistic people and those who are in need of companionship

This recommended type has more Industrial Impact to HRI This combination of methodologies ensures a good timing model for controlling multiple robots in a Human Robot Team (HRT) modelling, and works well with task performance metrics

Used for the highly technical system, especially for controlling multiple-robot team system, for multitasking and presentation purposes

above which have highlighted the strengths, weaknesses and uniqueness as well as the characteristics of each evaluation (primary and non-primary) and assessment methodology on HRI, another 2 tables are deployed as below to illustrate the contributions of our two strongly recommended hybrids of HRI methodologies. 7. Conclusions and discussions From the trends of HRI technology advancement and evolutionary optimization over the years, Table 8 above summarizes the contributions of this review paper to the societies as well as to the Robotics, AI and IT industries. In Section 6, we have discussed our new insights for future HRI, our inspirations, new vision for future research on modelling HRI and our proposed directives, including new approaches on newly recommended hybrid approaches. The goal of this review paper is to extensively explore almost all the HRI assessment and evaluation methodologies done for assessing and evaluating the HRI, where their aims are not just to substitute humans’ care with robotic care. The intentions of these past researchers’ work, however, are to provide the much–needed care where it is currently lacking in the HRI, and where the gap in the available care will subsequently increase due to the recognized demographic trends reviewed and discussed so far. Our recommended Type I has more social impacts because this hybrid of HRI methodologies focuses in solving the weaknesses of social integrated benefits during the HRI after surveying the past HRI methodologies (i.e. from UTAUT Model to Integrated Humans’ Intention Model). For our recommended Type II, this hybrid of HRI methodologies focuses on solving the weaknesses of technology advancement such as platform sensors, fuzzy tracking and integrated controllers during the HRI after surveying the past HRI methodologies (i.e. from UTAUT Model to Fuzzy Integrated Model). As a summary for our reviewing work, creating robots that are capable of estimating friendship, emulating empathy, understanding humans, learning from past experience and continuously improving from past learning approaches, is a very important step towards having those created robots as parts of our daily lives!! This paper has extensively reviewed the HRI assessment and evaluation methodologies so as to analyze if these approaches or methodologies on assessing and evaluating the HRI do improve or enhance over the years! This paper has also presented the important elements needed for assessing and evaluating humans’ acceptance towards robots, and how these methodologies are applied for modelling of HRI, especially on assistive social robots types. Our future review work includes exploring researches done on developing real-world experimental design in which the HRI models and modelling approaches as discussed in this paper can be further tested and enhanced. Our future research work will also include reviewing the verification of the test-beds and testing protocols, together with the assessment and evaluation methodologies, that have ever been done by the researchers on the robots discussed so far. The main contribution of this review paper is that we have proposed our new insights and inspirations


339

Table 8 Contributions of our strongly recommended HRI assessment and evaluation methodologies to the society and industry.

Our Strongly Recommended Hybrids of HRI Assessment and Evaluation Methodologies Recommendation Type I (Assessment Methodologies) (1) UTAUT Model (with Empathic Modelling); (2) Godspeed Five Key Concepts Modelling; (3) HRI Model with HCI and CSCW incorporated; (4) Model of integrated humans’ intentions, e.g. Haptic channel or the like (Evaluation Methodologies) (1) Self- Assessments Subjective Evaluation; (2) Psycho-Behavioural Measurements Recommendation Type II (Assessment Methodologies) (1) UTAUT Model (with Tele-operation Model ling); (2) Timing Model; (3) Human Robot Team (HRT) Model (4)Fuzzy Integrated Model (Evaluation Methodology) Task Performance Metrics

Type(s) of impact from years 2000 to 2014

More on Social Impact

More on Industrial Impact

Contributions of each of our recommended combinations of methodologies to the Society and Industry - Strengths of our recommended methodologies as compared with those surveyed from the types of commonly adopted methodologies since the year 2000 till 2014 After reviewing from the trends of Assessment and Evaluation Methodologies used during HRI (2000-2014), our recommended Type I increases the ease and pleasantness of HRI by advanced HCI and CSCW techniques, as well as shared humans’ intentions through integrated model such as Haptic Channel or feedback system. The trends of advancement is as below:2000-2003 2004-2009 2010-2012 2013 till now → UTAUT →Robot Behavioural → HRI model & → Intention Integration Model (with Adaptation Model HCI & CSCW or the like with the Empathic and the like or similar hybrids hybrid of the 3 elements) stated in 1st column more robot’s behavioural adaptation more HCI & CSCW components more intention integration features more shared human’s intention Contributions in solving the weaknesses of the commonly adopted HRI methodologies (from the year 2000 till 2014) After reviewing from the trends of Assessment and Evaluation Methodologies used during HRI (2000-2014), our recommended Type II is used for the highly technical system, especially for controlling multiple-robot team system, such as by using type-2 and type-1 Fuzzy Tracking Controllers or Fuzzy Integrator or the like. The trends of advancement is as below:2009-2012 2013 till now → 2000-2003 2004-2008 UTAUT → HRT model or → Temporal → Fuzzy Integrator tracking Model (with Human Control Awareness + or Gaze Controllers via Tele-operation) Gaze Models fusion or etc., or the like more multi-& autonomous control more temporal awareness more fuzzy tracking & integration Contributions in solving the weaknesses of the commonly adopted HRI methodologies (from the year 2000 till 2014)

for current and future HRI methodologies. These have significant social and industrial impacts. We have recommended two major types of hybrids for HRI assessment and evaluation methodologies in order to model HRI in a much easier, more pleasant, attractive and efficient, as well as more humans’ intentions integrated mode. Acknowledgements This research Project is proudly and mainly supported by the High Impact Research (HIR) Grant at UM.C/625/1/HIR/ MOHE/FCSIT/10 from University of Malaya (URL: www.um.edu.my), mainly from the Ministry of Higher Education under the Federal Malaysian Government Funding, Kuala Lumpur, Malaysia. In addition, this research is also supported by the funding from the eScienceFund, under the Ministry of Science, Technology and Innovation (MOSTI), for University of Malaya. The grant number for this research Project is 01-01-03-SF0661. This funded research is titled ‘AGED WELLNESS AUGMENTED BY EMPATHIC ENABLER (AWARE)’. References [1] R.C. Arkin, M. Fujita, T. Takagi, R. Hasegawa, An ethological and emotional basis for human–robot interaction, Robot. Auton. Syst. 42 (3–4) (2003) 191– 201. [2] C. Bartneck, T. Kanda, O. Mubin, A. AlMahmud, Does the design of a robot influence its animacy and perceived intelligence?, Int J. Soc. Robot. 1 (2) (2009) 195–204. [3] C. Bartneck, D. Kulic, E. Croft, easuring the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots, in: Workshop on Metrics for Human–Robot Interaction, Amsterdam, 2008, pp. 37–44. [4] C. Bartneck, T. Suzuki, T. Kanda, T. Nomura, The influence of people’s culture and prior experiences with AIBO on their attitude towards robots, Artif. Intell. Soc. 21 (1–2) (2007) 217–230. [5] C.L. Bethel, R.R. Murphy, Use of large sample sizes and multiple evaluation methods in human–robot interaction experimentation, in: AAAI, Association for the Advancement of Artificial Intelligence, 2009, pp. 1–8. [6] C.L. Bethel, C. Bringes, R.R. Murphy, Non-facial and non-verbal affective expression in appearance-constrained robots for use in victim management: robots to the rescue! in: The 4th ACM/IEEE International Conference on Human–Robot Interaction (HRI2009), 2009.

340


[7] C.L. Bethel, K. Salomon, J.L. Burke, R.R. Murphy, Psycho-physiological experimental design for use in human–robot interaction studies, in: The 2007 International Symposium on Collaborative Technologies and Systems (CTS 2007), IEEE, Orlando, FL, 2007. [8] C.L. Bethel, K. Salomon, R.R. Murphy, J.L. Burke, Survey of psychophysiology measurements applied to human–robot interaction, in: 16th IEEE International Symposium on Robot & Human Interactive Communication, 2007. [9] T. Bickmore, R.W. Picard, Towards caring machines, in: Proceedings of Computer–Human Interaction (CHI’04), Vienna, 2004, pp. 1489–1492. [10] T. Bickmore, D. Schulman, Practical approaches to comforting users with relational agents, in: Proceedings of Human–Computer Interaction 2007 (CHI’07), San Jose, CA, 2007. [11] T.W. Bickmore, L. Casuro, K. Clough-Gorr, T. Heeren, It’s just like you talk to a friend’ relational agents for older adults, Interact. Comput. 17 (6) (2005) 711–735. [12] S. Brave, C. Nass, K. Hutchinson, Computers that care: investigating the effects of orientation of emotion exhibited by an embodied computer agent, Int. J. Hum. Comput. Stud. 62 (2) (2005) 161–178. [13] J.L. Burke, R.R. Murphy, M.D. Coovert, D.L. Riddle, Moonlight in Miami: a field study of human–robot interaction in the context of an urban search and rescue disaster response training exercise, Hum.–Comp. Interact. 19 (2004) 85–116. [14] J.L. Burke, R.R. Murphy, D.R. Riddle, T. Fincannon, Task performance metrics in human–robot interaction: taking a systems approach, Perform. Metrics Intell. Syst. (2004) [15] S. Calinon, Z. Li, T. Alizadeh, N. Tsagarakis, D. Caldwell, Statistical dynamical systems for skills acquisition in humanoids, in: Proceedings of the International Conference on Humanoid Robots, 2012, pp. 323–329. [16] J. Cassell, C. Pelachaud, N.I. Badler, M. Steedman, B. Achorn, T. Beckett, B. Douville, S. Prevost, M. Stone, Animated conversation: rule-based generation of facial expression, gesture and spoken intonation for multiple conversational agents, in: Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH ’94), 1994, pp. 413–420. [17] O. Castillo, P. Melin, Optimization of type-2 fuzzy systems based on bio-inspired methods: a concise review, Inform. Sci. J. 206 (1) (2012) 1–19. [18] O. Castillo, R. Martinez-Marroquin, P. Melin, F. Valdez, J. Soria, Comparative study of bio-inspired algorithms applied to the optimization of type-1 and type-2 fuzzy controllers for an autonomous mobile robot, Inform. Sci. J. 192 (2012) 19–38. [19] N.R. Cázarez-Castro, L.T. Aguilar, O. Castillo, Designing type-1 and type-2 fuzzy logic controllers via fuzzy Lyapunov synthesis for non-smooth mechanical systems, Eng. Appl. Artif. Intell. 25 (5) (2012) 971–979. [20] A. Cesta, G. Cortellessa, M.V. Giuliani, F. Pecora, M. Scopelliti, L. Tiberio, Psychological implications of domestic assistive technology for the elderly, Psycho-Neurol. J. 5 (3) (2007) 229–252. [21] A. Cesta, G. Cortellessa, F. Pecora, R. Rasconi, Supporting interaction in the RoboCare intelligent assistive environment, in: Proceedings of the AAAI Spring Symposium on Interaction Challenges for Intelligent Assistants, Stanford, USA, 2007, pp. 18–25. [22] F. Chersi, Learning through imitation: a biological approach to robotics, IEEE Trans. Auton. Ment. Dev. 4 (3) (2012) 204–214. [23] T. Chesney, An acceptance model for useful and fun information systems, Hum. Technol. 2 (2) (2006) 225–235. [24] E. Clarkson, R.C. Arkin, Applying heuristic evaluation to human–robot interaction systems, in: FLAIRS Conference, 2007, pp. 44–49. [25] M.D. Cooney, T. Kanda, A. Alissandrakis, H. Ishiguro, Designing enjoyable motion-based play interactions with a small humanoid robot, Int. J. Soc. Robot. 6 (2) (2014) 172–193. [26] M.D. Cooney, S. Nishio, H. Ishiguro, Designing robots for well-being: theoretical background and visual scenes of affectionate play with a small humanoid robot, Lovotics 1 (1) (2014) 1–9. [27] H. Cramer, J. Goddijin, B. Wielinga, V. Evers, Effects of (in) accurate empathy and situational valence on attitudes towards robots, in: ACM/IEEE International Conference on Human–Robot Interaction, ACM, 2010, pp. 141–142. [28] K. Dautenhahn, I. Werry, A quantitative technique for analyzing robot–human interactions, in: Proceedings of the IEEE/RST International Conference on Intelligent Robots and Systems, Lausanne, 2002, pp. 1132–1138. [29] K. Dautenhahn, B. Ogden, T. Quick, From embodied to socially embedded agents – implications for interaction-aware robots, Cognit. Syst. Res., Spec. Iss. Sit. Embod. Cognit. 3 (3) (2002) 397–428. guest-editor: Tom Ziemke, Elsevier. [30] K. Dautenhahn, M. Walters, S. Woods, K.L. Koay, C.L. Nehaniv, How may I serve you?: a robot companion approaching a seated person in a helping context, in: Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human–Robot Interaction 2006 (HRI2006), 2006, pp. 172–179. [31] K. Dautenhahn, M. Walters, S. Woods, K.L. Koay, C.L. Nehaniv, A. Sisbot, R. Alami, T. Simeon, How may I serve you? A robot companion approaching a seated person in a helping context, in: The 1st ACM SIGCHI/SIGART Conference on Human–Robot Interaction (HRI 2006), ACM Press, New York, NY, Salt Lake City, UT, USA, 2006, pp. 172–179. [32] M.H. Davis, Measuring individual differences in empathy: evidence for a multidimensional approach, J. Pers. Soc. Psychol. 44 (1) (1983) 113–126. [33] P. Ekman, Facial expressions, Handbook of Cognition and Emotion, John Wiley & Sons Ltd., New York, 1999. [34] P. Ekman, Facial expressions of emotions, Annu. Rev. Psychol. 20 (1979) 527–554. [35] P. Ekman, Universals and cultural differences in facial expressions of emotion, in: Nebraska Symposium on Motivation, University of Nebraska Press, 1971. [36] D.G. Elmes, B.H. Kantowitz, H.L. Roediger III, Research Methods in Psychology, eighth ed., Thomson-Wadsworth, Belmont, CA, 2006. [37] T. Fong, I. Nourbakhsh, K. Dautenhahn, A survey of socially interactive robots, Robot. Auton. Syst. (2003) 143–166 [38] J. Forlizzi, How robotic products become social products: an ethnographic study of cleaning in the home, in: Proceedings of the 2nd ACM/IEEE International Conference on Human Robot Interaction (HRI 2007), ACM/IEEE, 2007, pp. 129–136. [39] M. Frank, J. Leitner, M. Stollenga, A. Förster, J. Schmidhuber, Curiosity driven reinforcement learning for motion planning on humanoids, Front. Neurorobot. 2014 (2014) 7–25. [40] B. Friedman, P. Kahn, J. Hagman, Hardware companions? what online AIBO discussion forums reveal about the human–robotic relationship, in: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Fort Lauderdale, USA, 2003. [41] R. Fry, G.F. Smith, The effects of feedback and eye contact on performance of a digit-encoding task, J. Soc. Psychol. 96 (1975) 145–146. [42] M. Fujita, AIBO: towards the era of digital creatures, Int. J. Robot. Res. 20 (10) (2001) 781–794. [43] D.F. Glas, T. Kanda, H. Ishiguro, N. Hagita, Tele-operation of multiple social robots, IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 42 (3) (2012) 530– 544. [44] D.F. Glas, T. Kanda, H. Ishiguro, N. Hagita, Temporal awareness in tele-operation of conversational robots, IEEE Trans. Syst., Man, Cybernet., Part A: Syst. Hum. 42 (4) (2012) 905–919. [45] R. Gockley, A. Bruce, J. Forlizzi, M. Michalowski, A. Mundell, S. Rosenthal, B. Sellner, R. Simmons, K. Snipes, A.C. Schultz , J. Wang, Designing robots for long-term social interaction, in: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2005), IEEE, 2005, pp. 2199–2204. [46] J. Goetz, S. Kiesler, A. Powers, Matching robot appearance and behavior to tasks to improve human–robot cooperation, in: IEEE Workshop on Robot and Human Interactive Communication (ROMAN’03), 2003. [47] B. Gonsior, S. Sosnowski, C. Mayer, J. Blume, B. Radig, D. Wollherr, K. Kuhnlenz, Improving aspects of empathy and subjective performance for HRI through mirroring facial expressions, in: Proceedings of the 19th IEEE International Symposium on Robot and Human Interactive Communication, 2011, pp. 1–7. [48] M.A. Goodrich, A.C. Schultz, Human–robot interaction: a survey, Found. Trends Hum.–Comp. Interact. 1 (3) (2007) 203–275. [49] R. Groten, D. Feth, R.L. Klatzky, A. Peer, The role of haptic feedback for the integration of intentions in shared task execution, IEEE Trans. Haptics 6 (1) (2013) 94–105. [50] E.T. Hall, The Hidden Dimension, Anchor Books, 1990.


341

[51] K. Hayashi, T. Kanda, T. Miyashita, H. Ishiguro, N. Hagita, Robot Manzai – robot conversation as a passive-social medium, Int. J. Humanoid Rob. 5 (1) (2008) 67–86. [52] K. Hayashi, T. Kanda, T. Miyashita, H. Ishiguro, N. Hagita, Robot Manzai – robots’ conversation as a passive-social medium, in: Proceedings of 2005 at the 5th IEEE – RAS International Conference on Humanoid Robotics, 2005, pp. 456–462. [53] K. Hayashi, D. Sakamoto, T. Kanda, M. Shiomi, S. Koizumi, H. Ishiguro, T. Ogasawara, N. Hagita, Humanoid robots as a passive social medium – a field experiment at a train station, in: Proceeding of the ACM/IEEE International Conference on Human–Robot Interaction 2007 (HRI2007), 2007, pp. 137– 144. [54] K. Hayashi, M. Shiomi, T. Kanda, N. Hagita, Are robots appropriate for troublesome and communicative tasks in a city environment?, IEEE Trans Auton. Ment. Dev. 4 (2) (2012) 150–160. [55] M. Heerink, Assessing Acceptance of Assistive Social Robots by Aging Adults, Thesis completed, University of Amsterdam, Netherlands, 2010, pp. 9– 22, 41–51, 87–99. [56] M. Heerink, B.J.A. Krose, V. Evers, B. Wielinga, Measuring acceptance of an assistive social robot: a suggested toolkit, in: The 18th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2009, 27 2009-October 2, 2009, pp. 528–533. [57] M. Heerink, B.J.A. Krose, V. Evers, B.J. Wielinga, Relating conversational expressiveness to social presence and acceptance of an assistive social robot, Virtual Reality 14 (1) (2010) 77–84. [58] M. Heerink, B.J.A. Krose, V. Evers, B. Wielinga, Studying the acceptance of a robotic agent by elderly users, Int. J. Assist. Robot. Mechatron. 7 (3) (2006) 33–43. [59] M. Heerink, B.J.A. Krose, B.J. Wielinga, V. Evers, Enjoyment intention to use and actual use of a conversational robot by elderly people, in: Proceedings of the 3rd ACM/IEEE International Conference on Human Robot Interaction (HRI 2008), ACM/IEEE, 2008, pp. 113–120. [60] M. Heerink, B.J.A. Krose, B.J. Wielinga, V. Evers, Human–robot user studies in eldercare: lessons learned, in: Smart Homes and Beyond, 4th International Conference on Smart Homes and Health Telematics 2006 (ICOST2006), Belfast, UK, 2006, pp. 31–38. [61] M. Heerink, B.J.A. Krose, B.J. Wielinga, V. Evers, Influence of social presence on acceptance of an assistive social robot and a screen agent by elderly users, Adv. Robot. 23 (14) (2009) 1909–1923. [62] M. Heerink, B.J.A. Krose, B.J. Wielinga, V. Evers, Measuring acceptance of assistive social agent technology by older adults: the Almere model, Int. J. Soc. Robot. 2 (3) (2010) 1–15. [63] M. Heerink, B.J.A. Krose, B.J. Wielinga, V. Evers, Measuring the influence of social abilities on acceptance of an interface robot and a screen agent by elderly users, in: Human–Computer Interaction 2009 (HCI 2009), Cambridge, UK, 2009, pp. 430–439. [64] M. Heerink, B.J.A. Krose, B.J. Wielinga, V. Evers, The influence of a robot’s social abilities on acceptance by elderly users, in: Proceedings of the Romanian-Academy (RO-MAN), Hertfordshire, UK, 2006, pp. 430–439. [65] F. Hegel, T. Spexard, T. Vogt, G. Horstmann, B. Wrede, Playing a different imitation game: interaction with an empathic android robot, in: Proceedings 2006 IEEE-RAS International Conference on Humanoid Robots (Humanoids06), 2006, pp. 56–61. [66] A.H. Hennington, B.D. Janz, Information systems and healthcare XVI: physician adoption of electronic medical records: applying the UTAUT model in a healthcare context, Commun. Assoc. Inf. Syst. 19 (1/5) (2007) 60–80. [67] D. Heylen, A. Nijholt, D. Reidsma, Determining what people feel and think when interacting with humans and machines: notes on corpus collection and annotation, in: Proceedings 1st California Conference on Recent Advances in Engineering Mechanics, Fullerton, USA, 2006, pp. 1–6. [68] C. Ho, K.F. MacDorman, Z.A. Pramono, Human emotion and the uncanny valley: a GLM, MDS, and Isomap analysis of robot video ratings, in: Proceedings of the 3rd ACM/IEEE International Conference on Human–Robot Interaction (HRI ’08), ACM, 2008. [69] K. Hone, Empathic agents to reduce user frustration: the effects of varying agent characteristics, Interact. Comput. 18 (2) (2006) 227–245. [70] Y.H. Hu, C.K. Loo, A generalized quantum-inspired decision making model for intelligent agent, Sci. World J. 2014 (2014) 1–8. ID No. 240983. [71] K. Itoh, H. Miwa, Y. Nukariya, M. Zecca, H. Takanobu, S. Roccella, M.C. Carrozza, P. Dario, T. Atsuo, Development of a bioinstrumentation system in the interaction between a human and a robot, in: International Conference of Intelligent Robots and Systems, 2006, pp. 2620–2625. [72] H. Ishiguro, T. Ono, M. Imai, T. Kanda, Development of an interactive humanoid robot ‘‘Robovie’’ – an interdisciplinary approach, Robot. Res. (2003) 179–191. [73] M. Jacobsson, J. Bodin, L.E. Holmquist, The see-Puck: a platform for exploring human–robot relationships, in: Proceeding of the 26th Annual SIGCHI Conference on Human Factors in Computing Systems, Florence, Italy, 2008, pp. 141–144. [74] M. Jacobsson, S. Ljungblad, J. Bodin, J. Knurek, L.E. Holmquist, GlowBots: robots that evolve relationships, in: The SIGGRAPH’07 International Conference of Emerging Technologies, San Diego, California, 2007, pp. 1–4. [75] B. Johnson, L. Christensen, Educational Research Quantitative, Qualitative, and Mixed Approaches, second ed., Pearson Education, Inc., Boston, MA, 2004. [76] M. Kamasima, T. Kanda, M. Imai, T. Ono, D. Sakamoto, H. Ishiguro, Y. Anzai, Embodied cooperative behaviours by an autonomous humanoid robot, in: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’04), 2004, pp. 2506–2513. [77] T. Kanda, H. Ishiguro, An approach for a social robot to understand human relationships: friendship estimation through interaction with robots, Interact. Stud. 7 (3) (2006) 369–403. [78] T. Kanda, H. Ishiguro, Reading human relationships from their interaction with an interactive humanoid robot, in: International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE), 2004. [79] T. Kanda, D.F. Glas, M. Shiomi, N. Hagita, Abstracting people’s trajectories for social robots to proactively approach customers, IEEE Trans. Rob. 25 (6) (2009) 1382–1396. [80] T. Kanda, T. Hirano, D. Eaton, H. Ishiguro, Interactive robots as social partners and peer tutors for children: a field trial, J. Hum.–Comp. Interact. 19 (1– 2) (2004) 61–84. [81] T. Kanda, H. Ishiguro, T. Ishida, Psychological analysis on human–robot interaction, in: The IEEE International Conference on Robotics and Automation (ICRA’01), 2001, pp. 4166–4173. [82] T. Kanda, H. Ishiguro, M. Imai, T. Ono, Development and evaluation of interactive humanoid robots, in: Proceedings of the IEEE International Conference, 2004, pp. 1839–1850. [83] T. Kanda, H. Ishiguro, M. Imai, T. Ono, K. Mase, A constructive approach for developing interactive humanoid robots, in: Proceeding of IEEE/RSJ International Conference of Intelligent Robots and Systems, 2002, pp. 1265–1270. [84] T. Kanda, H. Ishiguro, T. Ono, M. Imai, K. Mase, Multi-robot cooperation for human–robot communication, in: The IEEE International Workshop on Robot and Human Communication (ROMAN2002), 2002, pp. 271–276. [85] T. Kanda, H. Ishiguro, T. Ono, M. Imai, R. Nakatsu, Development and evaluation of an interactive humanoid robot Robovie, in: The Proceedings of IEEE International Conference on Robotics and Automation, 2002, pp. 1848–1855. [86] T. Kanda, T. Miyashita, T. Osada, Y. Haikawa, H. Ishiguro, Analysis of humanoid appearances in human–robot interaction, IEEE Trans. Rob. 24 (3) (2008) 725–735. [87] T. Kanda, S. Nabe, K. Hiraki, H. Ishiguro, N. Hagita, Human friendship estimation model for communication robots, Auton. Robots 24 (2) (2008) 135– 145. [88] T. Kanda, S. Nishio, H. Ishiguro, N. Hagita, Interactive humanoid robots and androids in children’s lives, Child., Youth Environ. 19 (1) (2009) 12–33. [89] T. Kanda, R. Sato, N. Saiwaki, H. Ishiguro, A two-month field trial in an elementary school for long-term human–robot interaction, IEEE Trans. Rob. 23 (5) (2007) 962–971. [90] T. Kanda, R. Sato, N. Saiwaki, H. Ishiguro, Friendly social robot that understands human’s friendly relationships, in: The Proceeding of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004, pp. 2215–2222. [91] T. Kanda, M. Shiomi, Z. Miyashita, H. Ishiguro, N. Hagita, A communication robot in a shopping mall, IEEE Trans. Rob. 26 (5) (2010) 897–913.

342


[92] C.D. Kidd, C. Breazeal, Human–robot interaction experiments: lessons learned, in: Proceedings of the AISB’05 Symposium Robot Companions: Hard Problems and Open Challenges in Robot–Human Interaction, 2005, pp. 141–142. [93] C.D. Kidd, W. Taggart, S. Turkle, A social robot to encourage social interaction among the elderly, in: Proceedings in 2006 of the IEEE International Conference on Robotics and Automation (ICRA 2006), 2006, pp. 3972–3976. [94] J. Klein, Y. Moon, R.W. Picard, This computer responds to user frustration: theory, design, and results, Interact. Comput. 14 (2) (2002) 119–140. [95] K. Koay, M. Walters, K. Dautenhahn, Methodological issues using a comfort level device in human–robot interactions, in: Proceedings of the 14th IEEE International Workshop on Robot and Human Interactive Communication (RO-MAN 2005), IEEE Press, 2005, pp. 359–364. [96] H. Kozima, M. Michalowski, C. Nakagawa, A playful robot for research, therapy, and entertainment, Int. J. Soc. Robot. 1 (2009) 3–18. [97] H. Kozima, C. Nakagawa, Y. Yasuda, Interactive robots for communication-care: a case-study in autism therapy, in: IEEE International Workshop on Robot and Human Interactive Communication (ROMAN 2005), 2005, pp. 341–346. [98] K. Kronander, A. Billard, Online learning of varying stiffness through physical human–robot interaction, in: International Conference on Robotics and Automation, 2012, pp. 1842–1849. [99] D. Kulic, E. Croft, Physiological and subjective responses to articulated robot motion, Robotica 25 (1) (2007) 13–27. [100] I. Leite, G. Castellano, A. Pereira, C. Martinho, A. Paiva, Modelling empathic behaviour in a robotic game companion for children: an ethnographic study in real-world settings, in: 7th ACM/IEEE International Conference on Human–Robot Interaction, 2012, pp. 367–374. [101] I. Leite, A. Pereira, S. Mascarenhas, C. Martinho, R. Prada, A. Paiva, The influence of empathy in human–robot relations, Int. J. Hum Comput Stud. 71 (3) (2013) 250–260. [102] S. Levine, V. Koltun, Continuous inverse optimal control with locally optimal examples, in The Proceedings of the 29th International Conference on Machine Learning (ICML’12), 2012, pp. 1–8. [103] C. Liu, P. Rani, N. Sarkar, Affective state recognition and adaptation in human–robot interaction: a design approach, in: International Conference on Intelligent Robots and Systems (IROS 2006), 2006, pp. 3099–3106. [104] P. Liu, D.F. Glas, T. Kanda, H. Ishiguro, N. Hagita, How to train your robot – teaching service robots to reproduce human social behavior, in: The 23rd International Symposium on Robot and Human Interactive Communication (RO-MAN 2014), UK, 2014, pp. 961–968. [105] R. Looije, F. Cnossen, M.A. Neerincx, Incorporating guidelines for health assistance into a socially intelligent robot, in: Proceedings of Romanian Academy (RO-MAN), Hatfield, UK, 2006, pp. 515–520. [106] M. Masashiro, On the uncanny valley, in: Proceedings of the Humanoids-2005 Workshop: Views of the Uncanny Valley, 2005. [107] A. Melendez, O. Castillo, Evolutionary optimization of the fuzzy integrator in a navigation system for a mobile robot, Recent Adv. Hybrid Intell. Syst. (2013) 21–31 [108] P. Melin, L. Astudillo, O. Castillo, F. Valdez, M. Garcia, Optimal design of type-2 and type-1 fuzzy tracking controllers for autonomous mobile robots under perturbed torques using a new chemical optimization paradigm, Expert Syst. Appl. 40 (8) (2013) 3185–3195. [109] A. Mora, D.F. Glas, T. Kanda, N. Hagita, A teleoperation approach for mobile social robots incorporating automatic gaze control and three-dimensional spatial visualization, IEEE Trans. Syst., Man, Cybernet. 43 (3) (2013) 630–642. [110] Y. Morales, T. Kanda, N. Hagita, Walking together: side by side walking model for an interacting robot, J. Hum.–Robot Interact. 3 (2) (2014) 50–73. [111] Y. Moriguchi, T. Kanda, H. Ishiguro, Y. Shimada, S. Itakura, Can young children learn words from a robot?, Interact Stud. 12 (1) (2011) 107–118. [112] M. Mori, Bukimi no Tami [The Uncanny Valley], Energy 7 (1970) 33–35. [113] L. Moshkina, R.C. Arkin, Human perspective on affective robotic behavior: a longitudinal study, in: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2005), 2005, pp. 2443–2450. [114] R. Murakami, Y. Morales, T. Kanda, H. Ishiguro, Destination unknown: walking side-by-side without knowing the goal, in: The Proceedings of the 9th ACM/IEEE International Conference on Human–Robot Interaction (HRI2014), 2014, pp. 1–15. [115] B. Mutlu, J. Forlizzi, Robots in organizations: the role of workflow, social, and environmental factors in human–robot interaction, in: ACM/IEEE International Conference on Human–Robot Interaction, ACM, 2008, pp. 287–294. [116] B. Mutlu, J.K. Hodgins, J. Forlizzi, A storytelling robot: modeling and evaluation of human-like gaze behavior, in: The 6th 2006 IEEE – RAS International Conference on Humanoid Robots (HUMANOIDS’06), IEEE, Genova, Italy, 2006, pp. 518–523. [117] B. Mutlu, T. Kanda, J. Forlizzi, J.K. Hodgins, H. Ishiguro, Conversational gaze mechanisms for humanlike robots, ACM Trans. Interact. Intell. Syst. 1 (2) (2012) 1–33 (Article No. 12). [118] B. Mutlu, S. Osman, J. Forlizzi, J.K. Hodgins, S. Kiesler, Task structure and user attributes as elements of human–robot interaction design, in: The 15th International Symposium on Robot and Human Interactive Communication (RO-MAN 2006), 2006, pp. 74–79. [119] B. Mutlu, A. Terrell, C. Huang, Coordination mechanisms in human–robot collaboration, in: The ACM/IEEE International Conference on Human–Robot Interaction (HRI) – on Collaborative Manipulation, 2013, pp. 1–6. [120] J. Nielsen, Enhancing the explanatory power of usability heuristics, in: Proceedings of the CHI 94 Conference on Human Factors in Computing Systems, ACM, Boston, MA, 1994. [121] T. Nomura, T. Kanda, T. Suzuki, K. Kato, Age difference and images of robots – social survey in Japan, Interact. Stud. 10 (3) (2009) 374–391. [122] D.R. Olsen, M.A. Goodrich, Metrics for evaluating human–robot interactions, in: Proceedings of (PerMIS), NIST’s Performance Metrics for Intelligent Systems Workshop, Gaithersburg, MA, 2003. [123] D.R. Olsen, S.B. Wood, Fan-out: measuring human control of multiple robots, in: Proceedings of the Conference on Human Factors in Computing Systems (CHI), 2004. [124] T. Ono, M. Imai, H. Ishiguro, A model of embodied communications with gestures between humans and robots, in: Proceedings of 23rd Annual Meeting Cognitive Science Society, 2001, pp. 732–737. [125] J.P. Otteson, C.R. Otteson, Effects of teacher gaze on children’s story recall, Percept. Mot. Skills 50 (1980) 35–42. [126] M. Pateraki, H. Baltzakis, P. Trahanias, Visual estimation of pointed targets for robot guidance via fusion of face pose and hand orientation, Comp. Vis. Image Understand., Elsevier 120 (2014) 1–13. [127] R. Picard, K. Liu, Relative subjective count and assessment of interruptive technologies applied to mobile monitoring of stress, Int. J. Hum Comput Stud. 65 (4) (2007) 361–375. [128] R. Picard, E. Vyzas, J. Healey, Toward machine emotional intelligence: analysis of affective physiological state, IEEE Trans. Pattern Anal. Mach. Intell. 23 (10) (2001) 1175–1191. [129] J. Preece, Y. Rogers, H. Sharp, D. Benyon, S. Holland, T. Carey, Human–Computer Interaction, Addison-Wesley, Wokingham, United Kingdom, 1994. p. 622. [130] P. Rani, N. Sarkar, C.A. Smith, L.D. Kirby, Anxiety detecting robotic system – towards implicit human–robot collaboration, Robotica 22 (1) (2004) 85– 95. [131] L.D. Riek, P. Robinson, Robot, rabbit, or red herring? Societal acceptance as a function of classification ease, in: The 17th International IEEE Symposium on Robot and Human Interactive Communication (RO-MAN 2008), Workshop Robots as Social Actors: Evaluating Social Acceptance and Societal Impact of Robotic Agents, IEEE, 2008. [132] L.D. Riek, P.C. Paul, P. Robinson, When my robot smiles at me: enabling human–robot rapport via real-time head gesture mimicry, J. Multimod. User Interf. 3 (1–2) (2010) 99–108. [133] B. Robins, K. Dautenhahn, R. te Boekhorst, A. Billard, Robotic assistants in therapy and education of children with autism: can a small humanoid robot help encourage social interaction skills? in: Access In the Information Society (UAIS), vol. 4(4), Springer-Verlag, 2005, pp. 2199–2204. [134] L. Rozo, P. Jimenez, C. Torras, A robot learning from demonstration framework to perform force-based manipulation tasks, Intel. Serv. Robot. 6 (1) (2013) 33–51.


343

[135] B. de Ruyter, P. Saini, P. Markopoulos, A.J.N. van Breemen, Assessing the effects of building social intelligence in a robotic interface for the home, Spec. Iss. IwC: Soc. Impact Emerging Technol. 17 (5) (2005) 522–541. [136] A. Sabelli, T. Kanda, N. Hagita, A conversational robot in an elderly care centre: an ethnographic study, in: ACM/IEEE International Conference on Human–Robot Interaction, ACM, 2011, pp. 37–44. [137] M. Saerbeck, T. Schut, C. Bartneck, M. Janse, Expressive robots in education: varying the degree of social supportive behaviour of a robotic tutor, in: Proceedings of Computer–Human Interaction 2010 (CHI 2010), ACM, 2010, pp. 1613–1622. [138] T. Saito, T. Shibata, K. Wada, K. Tanie, Relationship between interaction with the mental commit robot and change of stress reaction of the elderly, in: Proceedings of IEEE CIRA, Kobe, Japan, 2003, pp. 16–20. [139] Y. Sakagami, R. Watanabe, C. Aoyama, S. Matsunaga, N. Higaki, K. Fujimura, The intelligent ASIMO: System overview and integration, in: Proceeding of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002, pp. 2478–2483. [140] K. Sakai, H. Sumioka, T. Minato, S. Nishio, H. Ishiguro, Motion design of interactive small humanoid robot with visual illusion, Int. J. Innov. Comput., Inform. Control, IJICIC 12/2013 9 (12) (2013) 4725–4736. [141] D. Sakamoto, K. Hayashi, T. Kanda, M. Shiomi, S. Koizumi, H. Ishiguro, T. Ogasawara, N. Hagita, Humanoid robots as a broadcasting communication medium in open public spaces, Int. J. Soc. Robot. 1 (2) (2009) 157–169. [142] S. Satake, T. Kanda, D.F. Glas, M. Imai, H. Ishiguro, N. Hagita, A robot that approaches pedestrians, IEEE Trans. Rob. 29 (2) (2013) 508–524. issue 01. [143] J. Scholtz, S. Consolvo, Toward a framework for evaluating ubiquitous computing applications, IEEE Pervasive Comput. 3 (2) (2004) 82–88. [144] J. Scholtz, Evaluation methods for human–system performance of intelligent systems, in: Proceedings of the 2002 Performance Metrics for Intelligent Systems (PerMIS) Workshop, National Institute of Standards and Technology, Gaithersburg, MD, 2002. [145] A.M.B.T.Z.G.B. SergeThill, Daniele Caligiore, Theories and computational models of affordance and mirror systems: an integrative review, Neurosci. Bio-behav. Rev. 37 (2013) 491–521. [146] J. Sherwood, Facilitative effects of gaze upon learning, Percept. Mot. Skills 2/3 (2) (1988) 1275–1278. [147] T. Shibata, K. Tanie, Physical and affective interaction between human and mental commit robot, in: Proceedings of the IEEE International Conference on Robotics and Automation, 2001, pp. 2572–2577. [148] K. Shihab, D.Y.Y. Sim, Development of a visualization tool for XML documents, in: International Journal of Computers, vol. 4(04), North Atlantic University Union (NAUN), USA, 2010, pp. 153–160. [149] K. Shihab, D.Y.Y. Sim, A.M. Shahi, Angur: a visualization system for XML documents, in: Proceeding of the 9th WSEAS International Conference on Telecommunications and Informatics (TELE-INFO’10), Italy, 2010, pp. 159–165. [150] M. Shiomi, T. Kanda, D.F. Glas, S. Satake, H. Ishiguro, N. Hagita, Field trial of networked social robots in a shopping mall, in: The 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009, pp. 2846–2853. [151] M. Shiomi, T. Kanda, S. Koizumi, H. Ishiguro, N. Hagita, Group attention control for communication robots, Int. J. Human. Robot. (IJHR) 5 (4) (2008) 587–608. [152] T. Shiwa, T. Kanda, M. Imai, H. Ishiguro, N. Hagita, How quickly should a communication robot respond?, Int J. Soc. Robot. 1 (2) (2009) 141–155. [153] T. Shiwa, T. Kanda, M. Imai, H. Ishiguro, N. Hagita, How quickly should communication robots respond? in: Proceedings of the 3rd ACM/IEEE International Conference on Human Robot Interaction (HRI 2008), ACM/IEEE, 2008, pp. 153–160. [154] P.K. Shukla, S.P. Tripathi, A new approach for tuning interval type-2 fuzzy knowledge bases using genetic algorithms, J. Uncert. Anal. Appl. 2 (4) (2014) 1–15. [155] D.Y.Y. Sim, Emerging convergences of HCI techniques for graphical scalable visualization, in: Proceedings of 7th International Conference on IT in Asia 2011 (CITA’11), 2011, pp. 1–8. [156] R. Sorbello, A. Chella, C. Cali, M. Giardina, S. Nishio, H. Ishiguro, Telenoid android robot as an embodied perceptual social regulation medium engaging natural human-humanoid interaction, Robot. Auton. Syst. 62 (09) (2014) 1329–1341. [157] M. Staudte, M.W. Crocker, Investigating joint attention mechanisms through spoken human–robot interaction, Cognition 120 (2011) 268–291. [158] A. Steinfeld, T. Fong, D. Kaber, M. Lewis, J. Scholtz, A. Schultz, M. Goodrich, Common metrics for human–robot interaction, in: Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human–Robot Interaction, 2006, pp. 33–40. [159] B. Sterling, S.L. Gaertner, The attribution of arousal and emergency helping: a bidirectional process, J. Exp. Soc. Psychol. 20 (1984) 286–296. [160] W.D. Stiehl, J. Lieberman, C. Breazeal, L. Basel, L. Lalla, M. Wolf, The design of the huggable: a therapeutic robotic companion for relational, affective touch, in: Proceedings of AAAI 2005 Fall Symposium on Caring Machines, AAAI, 2005. [161] K. Stubbs, P. Hinds, D. Wettergreen, Autonomy and common ground in human–robot interaction: a field study, IEEE Intell. Syst. 22 (2) (2007) 42–50. [162] J. Sung, H. Christensen, R. Grinter, Robots in the wild: understanding long-term use, in: The ACM/IEEE International Conference on Human–Robot Interaction (HRI), ACM, 2009, pp. 45–52. [163] J. Sung, R. Grinter, H. Christensen, L. Guo, Housewives or technophiles? Understanding domestic owners, in: Proceedings of the 3rd ACM/IEEE International Conference on Human Robot Interaction (HRI 2008), ACM/IEEE, 2008, pp. 129–136. [164] W. Taggart, S. Turkle, C. Kidd, An Interactive Robot in a Nursing Home: Preliminary Remarks, Towards Social Mechanisms of Android Science, Cognitive Science Society, Stresa, Italy, 2005. pp. 56–61. [165] T. Tamura, S. Yonemitsu, A. Itoh, D. Oikawa, A. Kawakami, Y. Higashi, T. Fujimooto, K. Nakajima, Is an entertainment robot useful in the care of elderly people with severe dementia?, J Gerontol. Ser. A: Biol. Med. Sci. 59 (1) (2004) 83–85. [166] A. Tapus, M.J. Mataric’, Emulating Empathy in Socially Assistive Robotics, American Association for Artificial Intelligence, 2006. pp. 1–4. [167] A. Tapus, M.J. Mataric’, Socially assistive robots: the link between personality, empathy, physiological signals, and task performance, Association for the Advancement of Artificial Intelligence in AAAI Spring 8 (2008) 1–8. [168] A. Tapus, M.J. Mataric’, User personality matching with hands-off robot for post-stroke rehabilitation therapy, in: Proceeding of International Symposium on Experimental Robotics (ISER’06), 2006. [169] A. Tapus, M.J. Mataric’, B. Scassellati, The grand challenges in socially assistive robotics, IEEE Robot. Autom. Magaz. Spec. Iss. Grand Chall. Robot. 14 (1) (2007) 35–42. [170] D. Veenstra, V. Evers, The development of an online research tool to investigate children’s social bonds with robots, Hum.–Robot Pers. Relation. 59 (2011) 19–26. [171] V. Venkatesh, F.D. Davis, A theoretical extension of the technology acceptance model: four longitudinal field studies, Manage. Sci. 46 (2) (2000) 186– 204. [172] V. Venkatesh, M.G. Morris, G.B. Davis, F.D. Davis, User acceptance of information technology: toward a unified view, MIS Quart. 27 (3) (2003) 425– 478. [173] K. Wada, T. Shibata, T. Saito, K. Tanie, Analysis of factors that bring mental effects to elderly people in robot assisted activity, in: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 2, 2002, pp. 1152–1157. [174] K. Wada, T. Shibata, T. Saito, K. Tanie, Effects of robot assisted activity to elderly people who stay at a health service facility for the aged, in: Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003), USA, 2003, pp. 2847–2852. [175] K. Wada, T. Shibata, T. Saito, K. Tanie, Psychological and social effects of robot assisted activity to elderly people who stay at a health service facility for the aged, in: Proceedings International Conference on Robotics and Automation, USA, 2003, pp. 3996–4001. [176] K. Wada, T. Shibata, T. Saito, K. Tanie, Psychological, physiological and social effects to elderly people by robot assisted activity at a health service facility for the aged, in: Proceedings of IEEE/RSJ International Conference on Advanced Intelligent Mechatronics (AIM 2003), Kobe, Japan, 2003. [177] K. Wada, T. Shibata, T. Saito, K. Tanie, Relationship between familiarity with mental commit robot and psychological effects to elderly people by robot assisted activity, in: Proceedings IEEE International Symposium on Computational Intelligence in Robotics and Automation, Kobe, Japan, 2003, pp. 113–118.

344


[178] Y. Wang, J.E. Young, Beyond ‘Pink’ and ‘Blue’: Gendered attitudes towards robots in society, in: The Proceedings of the ACM SIGCHI Conference on the Significance of Gender for Modern Information Technology (GenderIT2014), 2014, pp. 1–10. [179] D. Watson, L.A. Clark, A. Tellegen, Development and validation of brief measures of positive and negative affect: the panas scales, J. Pers. Soc. Psychol. 54 (6) (1988) 1063–1070. [180] P. Wu, C. Miller, Results from a field study: the need for an emotional relationship between the elderly and their assistive technologies, Found. Augm. Cognit. 11 (2005) 889–898. [181] F. Yamaoka, T. Kanda, H. Ishiguro, N. Hagita, A model of proximity control for information-presenting robots, IEEE Trans. Robot. 26 (1) (2010) 187– 195 (short paper). [182] H.A. Yanco, J.L. Drury, J. Scholtz, Beyond usability evaluation: analysis of human–robot interaction at a major robotics competition, Hum. Comp. Interact. 19 (2004) 117–149. [183] K. Zheng, D.F. Glas, T. Kanda, H. Ishiguro, N. Hagita, Designing and implementing a human–robot team for social interactions, IEEE Trans. Syst., Man Cybernet.: Syst. 43 (4) (2013) ; 843–859 (issue 01). [184] K. Zheng, D.F. Glas, T. Kanda, H. Ishiguro, N. Hagita, How many social robots can one operator control? in: Proceedings of ACM/IEEE 6th Annual Conference on Human–Robot Interaction, Lausanne, Switzerland, 2011, pp. 379–386. [185] K. Zheng, D.F. Glas, T. Kanda, H. Ishiguro, N. Hagita, Supervisory control of multiple social robots for conversation and navigation, Trans. Control Mech. Syst. 3 (2) (2014) 76–92.