Minimal Physical Features Required for Social Robots - SAGE Journals

Proceedings of the Human Factors and Ergonomics Society 59th Annual Meeting - 2015

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Minimal Physical Features Required for Social Robots Molly C. Martini, Rabia Murtza, & Eva Wiese George Mason University Social robotics strives to create robots that enable social interactions similar to those experienced between two humans with the goal to increase performance in human-robot interaction (HRI). This is often achieved by designing robots that create an illusion of intentionality either through biologically inspired design or functional design in which the agent mirrors the cognitive or physical aspects of a human. The current study focused on functionally inspired design with the intent to learn what minimal features need to be included in the physical design of a social robot in order for it to appear as an intentional agent. Two groups of three and four participants respectively were lead through a design thinking workshop in which they brainstormed and ranked the physical features and expected interactions of social and non-social robots. They were asked to sketch ideal and minimum versions of each type of robot which were then evaluated on the degree to which different mental states were attributed to the robot (as only an intentional agent can have a mind and therefore mental states). Results showed that the minimum features required for participants to attribute mental states to a robot include an emotive head with eyes and a mouth. This minimal feature set can be utilized by social roboticists to aid in future designs in order to save both time and monetary resources.

Copyright 2015 Human Factors and Ergonomics Society. DOI 10.1177/1541931215591312

INTRODUCTION The ability to think upon and infer the internal thoughts of others is a developmental skill humans employ to aid in social interaction (Frith & Frith, 2006). Attempting to capitalize on this innate function known as mentalizing, social roboticists are trying to design robots that are seen as intentional agents capable of having their own thoughts and feelings in order to facilitate a more natural and enjoyable interaction (Waytz, Gray, Epley, & Wegner, 2010). Typically, an anthropomorphic design is taken to induce intentionality onto a mechanistic agent. A design is anthropomorphic if it is representative of a human, be it in the underlying mechanisms that guide the agent’s behavior or in its external physical features. In other words, anthropomorphic robots can be either biologically inspired or functionally designed (Fong, 2003). Biologically inspired design attempts to replicate human cognition, perception, and development in a mechanistic agent. Two of the more famous biologically designed robots are Kismet and Cog from the MIT Artificial Intelligence Laboratory (Breazeal & Scassellati, 2000; Scassellati 1998). Kismet has been engineered to mirror infant-like social interactions with a human caregiver by having a built-in function that measures interaction intensity. Different thresholds then modulate Kismet’s reactions serving as a proxy for how a human infant would naturally signal to its caregiver when it was tired, bored, frustrated, etc. (Breazeal & Scassellati, 2000). The other robot, Cog, has been used as an engineering test bed to create a robotic theory of mind, in which the robot attempts to infer the thoughts of others, and is built based off of preexisting theories in regards to human cognition (Scassellati, 1998). Biologically inspired agents therefore serve both as a research tool to investigate different developmental, perceptual, and cognitive theories as well as attempt to increase human-robot interaction (HRI) by having the underlying engineering architecture of the robot mirror that of a human. Alternatively, functional design is concerned not with the internal structures of a given agent, but with the exterior. Specifically, the emphasis is placed on building an agent that out-

wardly appears to be a social being. One example of a functionally designed robot is Sparky, a user controlled box-like embodied robot with an emotive face able to mirror human expressions such as happiness or fear (Scheef, Pinto, Rahardja, Snibbe, & To, 2000). In order to achieve a functional design, three design techniques are commonly employed. The first is a human-computer interaction (HCI) design approach which focuses on exploring the interactions between humans and different computer representations. This approach was used in the creation of Sparky. The second is a systems engineering approach where the desired functionality of the agent informs the design, such as ensuring that a hospital assistant robot is capable of lifting and moving incapacitated patients. Lastly, the third approach uses iterative design to arrive at an ideal model. The goal of functionally designed robots is therefore one of facilitating HRI through outward design. The design of a given robot, be it social or non-social, is important because it affects users’ expectations in regards to how the robot is suppose to behave and how one is suppose to interact with the agent (Duffy, 2003). For instance, a past study examining the relationship between participants’ design of a military robot and their reported knowledge of the agent found that people who drew more anthropomorphic robots reported having more knowledge about their military robotic interaction partner in a given task compared to participants who drew more mechanistic looking agents (Osoky, Philips, Shuster, & Jentsch, 2013). Furthermore, the design of an agent cues interaction partners as to what type of stance they should take in order to explain their partners’ actions and react appropriately (Dennett, 1987). Upon viewing a robot, humans can take a design stance, a physical stance, or an intentional stance. If a design stance is taken then it is presumed that the agent was created for a specific function, such as dropping a ball, similar to a systems engineering approach. If a physical stance is taken then the assumption is that any action conducted by the agent is a result of basic physics, i.e., the robot dropped the ball because of mechanistic springs and gravity. If an intentional stance is taken however, then it is assumed that the agent has a mind and deliberately decided to drop the ball.


In turn, an interaction partner taking an intentional stance might infer that the agent is waiting for the ball to be picked up and react to fulfill this perceived goal. This reaction is unlikely to occur if a design or physical stance is taken and is therefore imperative for successful social interactions. A more intuitive and positive HRI can therefore be fostered by designing robots that promote an intentional stance. While biologically inspired design is heavily researched, the external design features required for mind attribution of social robots remains unclear. If there is a specific set of features that can help people quickly distinguish between social and non-social agents, then both monetary and time-based resources can be saved. The current study aims to learn what expectations laypeople have regarding the design of social and non-social robots through the use of a design thinking workshop in order to address this question. Design thinking is a cyclic human-centered approach involving brainstorming and the rapid prototyping of ideas to meet users’ needs (Brown, 2008). The process starts with inspiration, moves to ideation, and ends with implementation. Inspiration comes from the introduction of a problem or a need. This leads to ideation, the process of generating and organizing possible solutions for the identified problem. Ideation is dependent on individuals’ ability to generate ideas and connect different concepts together through brainstorming known as explorative and combinatorial creativity respectively. Implementation then occurs when solutions are realized in a tangible manner, e.g., prototypes that can be used in the next iterative cycle (Maiden, Robertson, & Gizikis, 2004). Following this model, the current study asked participants to define a social and non-social robot, then brainstorm and rank the physical features and interactions expected for each agent. These ideas were then implemented by having participants sketch ideal and minimum designs for each robot. Designs were then evaluated on their ability to induce mentalization as measured by the degree to which the agent was perceived to possess different mental states. If a set of physical features increases the likelihood of an intentional stance being taken towards a robotic agent, then social roboticists should consider including that minimal set of features into future robotic design to increase HRI. METHOD Participants Participants were comprised of one male and six female undergraduate and graduate students from George Mason University (Ageavg = 22.14). All participants gave their consent to participate and could either volunteer freely without compensation or receive course credit for their participation. After obtaining consent, participants were split into two groups, Group A and Group B, made up of three and four people respectively. Group size was chosen to be small in order to make participants more engaged in the group discussions (Paulus, 2000). The uneven balance was simply due to one participant failing to show.

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Materials Design Materials. Post-it notes, pens, and pencils were available for writing and drawing ideas. Mirroring Stanford’s School of Design’s Crash Course in Design Thinking (http://dschool.stanford.edu/dgift/#crash-course-video), paper templates were created using 22 x 16 inch pieces of paper. The papers were split into two main sections, “individual” and “group,” for brainstorming both the features and interactions of a social and non-social robot. The individual section was for individual ideas to be placed while the group section provided space to merge those ideas together and collectively create new ones (See Figure 1). Four blank sheets without divisions were given to each group for the four different sketches (social ideal, social minimum, non-social ideal, nonsocial minimum).

Figure 1. Example of a pre-defined page and study layout.

Evaluation Form. Each sketch was evaluated on a 17-item questionnaire using a 7-point Likert scale with 1 = Definitely Not to 7 = Definitely followed by two free-response items. The first sixteen items of the questionnaire were adapted from a prior survey used to assess the mental states attributed to different images of various agents along a mechanistic to full human morphing spectrum (Martini, Gonzalez, & Wiese, 2015). The eight different mental states assessed were Agency, Animacy, Theory of Mind, Emotions, Goals and Preferences, Cognitive Skills, Social Communication Skills, and Sense of Humor (please see Martini, Gonzalez, & Wiese, 2015, for more detail). The seventeenth item was not part of the mental states survey and asked the participant to rate the degree to which a sketch looked like either a social or nonsocial robot. The evaluation for each sketch then concluded with participants writing down brief descriptions as to why they thought the sketch was a good depiction of a given robot and why it was a bad depiction. Demographic Form. Aside from age and gender, five additional items were included to gauge participants’ prior interaction experience with robots, general design knowledge, robot design knowledge, science-fiction exposure, and how they would feel interacting with social robots within society. The first four items were on a 5-point Likert scale with 1 = None at all and 5 = A lot. The last item regarding interaction with social robots within society was also on a 5-point Likert scale, but with the anchors 1 = I would hate it and 5 = I would love it. These measures were collected in case the scores modulated the ratings of different mental states.


Procedure The two groups were split between two different rooms upon arrival. Participants were told the goal of the study at the beginning of the workshop and were each given a Post-it notepad. As a warm-up, each participant wrote down an activity and an object on a separate Post-it note then passed it to their right and left respectively. Each person then briefly explained how their new combination made sense. After the warm-up, participants worked first as individuals and then as a group to define a social robot. The only clarification given was that a social robot has a mind, while a nonsocial robot has no mind. Next, participants worked individually to brainstorm the physical features of a social robot. Then working as a group, participants rank ordered their generated features list from the most important to the least important. Next, participants worked to brainstorm the types of interactions expected of a social robot again working first as individuals and then ranked the listed interactions as a group. Using the ranked lists the group created an ideal sketch of a social robot with one volunteer acting as the drawer for the group. The group then worked on creating a sketch with minimal features. Throughout the workshop all individual work was timed for 90 seconds and all group work was timed for 150 seconds to limit fixation on any single idea (Jansson & Smith, 1991). The same process was repeated for the non-social robot so that each group ended with four sketches: social ideal, social minimum, non-social ideal, and non-social minimum. The groups then switched their four sketches and evaluated the drawings using one evaluation form per person per sketch. Lastly, participants filled out demographics. The total session took one hour to complete. RESULTS Definitions & Design Non-social robots. Non-social robots were defined as being task specific and capable of only minimal interaction facilitated mainly through information input devices like buttons. Group A defined a non-social robot as one that “has a robotic voice which is conveyed through a speaker, is factory machine-like, has rigid movements, and is used to perform basic tasks (i.e. housework).” Similarly, Group B defined a nonsocial robot as a “functional, non-interactive robot with specific purpose/use.” Consequently, both groups had similar designs and interaction expectations. Each group choose to depict square, boxlike computers with the main difference between the minimum and ideal designs being the addition of more input devices such as a touchscreen, buttons, and scanners (See Figure 2). Interactions were expected to be limited as all agent actions, be it physical movement or response output, was inferred to be dependent on the user inputting information. In sum, the nonsocial robots were seen not as interaction partners, but as machines able to do basic input/output functions (see Table 1 for a full list of expected interactions).

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Social robots. The definitions for a social robot were more varied across the two groups. Group A defined a social robot as an agent that “should be emotional, have eyes and welcoming features, and have a human-like voice,” whereas Group B defined it as one that is “animated to help other people, but has limitations for safety and learns from the environment (adaptable).” The variation in the definitions may indicate that the definition for a social robot is more flexible and can encompass a wider range of expectations compared to a non-social robot.

Figure 2. Non-social designs for both groups with minimum designs on the left and ideal designs on the right.

Despite the differences in definitions the social designs of the two groups were similar in feature and interaction descriptions, though not in aesthetics (See Figure 3). Both groups’ minimum designs were essentially a head with eyes and an mouth whereas the ideal designs included a full body capable of a larger degree of motion and additional features, such as eyebrows, to better convey emotion. The emphasis placed on facial features was most likely connected to the interaction expectation of social robots being emotive, communicative agents (see Table 1 for full list of interaction expectations). Unlike their non-social counterparts, social robots were given roles rather than specific tasks that depended upon social interaction with another being. For instance, offering companionship and empathizing with others. Design Summary. The results suggest that participants distinguish social robots from non-social robots in not only having a mind (our given definition), but in their ability to convey emotion and communicate with others. Consequently, the physical features expected on a social robot enable the agent to converse without needing direct manual input from the user. Additionally, in describing the aesthetics of social robots both groups said that the agent should be “unimposing” and “approachable” with a “good color that is not harsh, metallic, or intimidating.” Table 2 summarizes the required set of physical features for each agent. Evaluation of Mental States Given the small sample size of the mixed design and that the data is subjective versus metric, a linear mixed effects regression analysis was chosen over a MANOVA. The lme4


package (Bates, Maechler, & Bolker, 2012) in R (R Core Team, 2012) was used to conduct the analysis.

Figure 3. Social designs for both groups with minimum designs on the left and ideal designs on the right.

Group A

Table 1. Ranked Interactions Expectations by Group Social

Non-social

Converses through language

Cannot apply to emotional tasks

Shows emotion/empathizes

User has to input information through buttons or a screen Answers only yes/no questions

Human-like inflection in voice Gives general updates (weather, news, social media, game scores, etc.) Companionship

Group B

Caregiver Teacher-student: sharing advice and knowledge questions Housekeeper

Movement dependent on user User inputs information for a function A forgotten presence “Robot-like” activities Misfires and requires repairs Dependent on programming

Table 2. Set of Physical Features Social Minimum

Ideal (in addition)

Head

Non-social Minimalistic form (rectangular shapes)

Eyes Mouth

Simple inputs/outputs

Eyebrows

More input/output (screens and buttons)

Body

Larger computer

The model included one categorical fixed effect which was the design itself (social minimum, social ideal, non-social minimum, non-social ideal) and specified random intercepts chosen from model testing for each participant, participants’ interaction experience with a robot, and participants’ general design knowledge. Contrast codes were used to set the social minimum design as the reference level to which the three oth-

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er designs were compared (see Table 3 for the social minimum mean ratings). Social minimum versus both ideal and minimum nonsocial designs. Significantly higher ratings of Animacy, Emotion, Social Communication Skills, and Humor were observed for the social minimum designs compared to the non-social designs (all bi ≥ -1.00, ti(20) ≥ -2.11, pi < 0.05). Additionally, the Cognitive Skills of the social minimum designs were reported as being significantly higher than the non-social minimums (b = -2.08, t(20) = -2.37, p < 0.03) and marginally different than the non-social ideals (b = -1.75, t(20) = -1.99, p < 0.06). No differences were found for Agency, Theory of Mind, and Goals and Preferences. Social minimum versus social ideal. The social ideal designs were rated significantly higher on four of the eight mental states: Animacy, Theory of Mind, Emotion, and Humor (all bi ≥ 1.33, ti(20) ≥ -2.11, pi < 0.05). No differences were found for the other four states: Agency, Social Communication Skills, Cognitive Skills, and Goals and Preferences. Table 3. Mental states ratings for the social minimum design reference level. States were rated on a 7-point Likert scale with 1 = Definitely Not to 7 = Definitely. M SD Agency 3.43 2.01 Animacy+ 3.57 2.03 Theory of Mind 2.71 1.87 Emotion+ 2.86 1.91 Goals and Preferences 2.79 1.44 Cognitive Skills+ 4.50 1.85 Social Communication Skills+ 3.71 1.91 Humor+ 3.36 2.11 + Significant differences compared to non-social designs (p < .05)

DISCUSSION Both pre-existing expectations regarding the mind of an agent as well as the external cues one receives in social interactions influences how an individual will interact with a robotic partner (Dennett, 1989; Duffy, 2003; Wiese, Wykowska, Zwickel, & Müller, 2012). The goal of this study was to figure out what those expectations regarding social interactions are and how they can be met through physical design in order to identify the minimal features required to make a robot appear as a social intentional agent. Using a design thinking approach, we found that individuals define non-social robots as task-specific computer devices that perform basic input-output functions whereas social robots are emotive, communicative embodied machines. These expectations translated into the sketches drawn for ideal and minimum versions of a non-social and social robot. While the designs between the two participant groups were similar in features and aesthetics for the non-social designs, more variability was seen aesthetically for the social designs. This makes sense as less stereotypes currently exist for social robots in everyday life compared to non-social agents which are often thought of as desktop computers. However the lack of a strong mental model for a social robot means that the rules of social interaction individuals draw upon when interacting with an agent become even more reliant on external


cues (Epley, Waytz, & Cacioppo, 2007). Therefore it is imperative that social roboticists utilize the external physical design of an agent to increase the likelihood of an intentional stance being taken to understand the robot's actions. The minimum features that appeared to increase the degree of mind attribution given to an agent were a head with eyes and a mouth that convey different emotions. This feature set was able to increase subjective measures of an agent’s perceived animacy, ability to experience emotion, ability to learn and strategize, ability to converse and socialize, and sense of humor. What is rather puzzling is that this set of features did not seem to make the agent appear as an actual intentional agent (theory of mind) with its own goals and preferences nor agency, which was defined as being aware of its actions and capable of making its own decisions. It was only when a social robot was given a more substantial body that it was perceived as having its own theory of mind. Thus, while a subset of mental states (e.g., emotions, humor, communication skills) seems to be modifiable via physical design, the presented findings suggest that more abstract mental concepts, such as agency, goals and preferences, and theory of mind require evidence of independent original thinking. Consequently, the current study is inconclusive regarding the question of whether or not physical design can trigger the attribution of particular, more abstract mental states to social robots. One possibility is that attribution of agency, goals and preferences, and theory of mind would be possible, but that a different minimal subset of design features are needed for these mental concepts compared to more specific ones, such as having a good sense of humor, having communication skills, or being able to have emotions. Given that specifically agency and goals and preferences require effectors that can actually execute actions, it makes sense to assume that a full-body design is needed in order for these mental concepts to be attributed to the robot. An alternative explanation would be that in order to make judgments about agency, action goals and intentions, participants not only need to perceive the robot, but also need to actually interact with the robot in order to build up a mental model about the robot’s internal states. One way to address this issue would be to use biologically inspired design in addition to physical design to better facilitate a more naturalistic interaction and then ask participants to describe the robot’s internal states to see which type of design has a stronger influence on mind attribution (Breazeal & Scassellati, 2000; Scassellati 1998). It is important to note though that these minimal features were created by a relatively young sample. Different populations will most likely have different expectations and design ideas that should be taken into account. With this limitation in mind, the next step proposed is to further validate the sketches on Mechanical Turk, an online marketplace where small amounts of money are paid to participants to answer surveys. This will allow observations to be collected from a larger more diverse population to better support the current conclusion that the minimal features of a head, eyes, and mouth lead to increased mind attribution. Finally, a real-life social interaction task between a human and a robotic partner with these minimal features should be conducted to ensure that increased HRI performance does in fact occur.

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Notwithstanding the work to be done in the future, the minimal features for social robots described in this study provide a base reference level that can be used by roboticists for future designs. By utilizing the minimal physical features of an emotive head, eyes, and mouth, resources can be saved and a more positive HRI can result from increasing the likelihood of an intentional stance being taken by the human interaction partner. References Bates, D.M., Maechler, M., & Bolker, B. (2012). lme4: Linear mixed-effects models using S4 classes. R package version 0.999999-0. Breazeal, C., & Scassellati, B. (2000). Infant-like social interactions between a robot and a human caregiver. Adaptive Behavior, 8(1), 49-74. Brown, T. (2008). Design thinking. Harvard business review, 86(6), 84. Dennett, D. C. (1987). The intentional stance. Cambridge, MA: MIT Press. Duffy, B. R. (2003). Anthropomorphism and the social robot. Robotics and autonomous systems, 42(3), 177-190. Epley, N., Waytz, A., & Cacioppo, J. T. (2007). On seeing human: a threefactor theory of anthropomorphism. Psychological review, 114(4), 864. Frith, C. D., & Frith, U. (2006). The neural basis of mentaliz ing. Neuron, 50(4), 531-534. Jansson, D. G., & Smith, S. M. (1991). Design fixation. Design studies, 12(1), 3-11. Maiden, N., Gizikis, A., & Robertson, S. (2004). Provoking creativity: Imagine what your requirements could be like. Software, IEEE, 21(5), 68-75. Martini, M. C., Gonzalez, C. A., & Wiese, E. (2015). Seeing minds in robots – Can agents with robotic appearance have human-like preferences? Manuscript in preparation. Ososky, S., Philips, E., Schuster, D., & Jentsch, F. (2013, September). A Picture is Worth a Thousand Mental Models Evaluating Human Understanding of Robot Teammates. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 57, No. 1, pp. 1298-1302). SAGE Publications. Paulus, P. (2000). Groups, Teams, and Creativity: The Creative Potential of Idea-generating Groups. Applied psychology, 49(2), 237-262. R Core Team (2012). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Scassellati, B. (1998). A binocular, foveated active vision system (Report No. 1628). Cambridge, MA: Massachusetts Institute of Technology Artificial Intelligence Lab. Scheeff, M., Pinto, J., Rahardja, K., Snibbe, S., & Tow, R. (2002). Experiences with Sparky, a social robot. In Socially Intelligent Agents (pp. 173-180). Springer US. Stanford School of Design. Virtual Crash Course in Design Thinking [video]] Retrieved from http://dschool.stanford.edu/dgift/#crash-course-video. Waytz, A., Gray, K., Epley, N., & Wegner, D. M. (2010). Causes and consequences of mind perception. Trends in cognitive sciences, 14(8), 383-388. Wiese, E., Wykowska, A., Zwickel, J., & Müller, H. J. (2012). I see what you mean: how attentional selection is shaped by ascribing intentions to others. PloS one, 7(9), e45391.