Methods for Studying GroupInteractions in HRI

Marynel Vazquez 1, Elizabeth J. Carter 3, Jodi Forlizzi 2,3,Scott E. Hudson 2,3, and Aaron Steinfeld 1

1 Robotics InstituteCarnegie Mellon UniversityPittsburgh, PA{marynel, steinfeld}@cmu.edu

2 HCI InstituteCarnegie Mellon UniversityPittsburgh, PA{forlizzi, scott.hudson}@cs.cmu.edu

3 Disney ResearchPittsburgh, PAliz.carter@disneyresearch.com

Copyright is held by the author/owner(s).CSCW’17, February 25 – March 1, 2017, Portland, Oregon.

AbstractIn recent years, we have conducted several Human-RobotInteraction (HRI) experiments with small groups ofpeople. To do so, we developed four different protocols toinvestigate human spatial behavior or trust in robots. Wenow look back at these efforts and highlight theopportunities and challenges of each experimentalmethod. We also describe various group phenomena thatwe observed during the interactions. By sharing ourexperience, we hope to inform the community of thelessons that we learned in HRI and emphasize theimportance of studying group interactions to enablerobots to operate in public human environments.

ACM Classification KeywordsH.1.2 [MODELS AND PRINCIPLES]: User/MachineSystems—Human factors

IntroductionStudying group encounters in Human-Robot Interaction(HRI) is important because these encounters are commonin human environments and induce interesting socialphenomena. For example, group spatial behavior canprovide information about user engagement in HRI.Likewise, social influence can potentially alter thedynamics of multiparty encounters.

In this paper, we look back at four different HRIexperiments that we conducted with small groups ofparticipants [9, 11, 8, 7]. Even though these experimentswere designed specifically to study human spatial behaviorand trust in robots, their protocols could be further usedto investigate other aspects of multi-party interactions.We highlight these opportunities as well as the challengesof each protocol in the next sections, starting with themost uncontrolled and challenging experiment. Indiscussing these experiences, we hope to convey thepotential of studying group interactions in HRI andencourage other researchers to work on this topic.

Experiment 1. Unanticipated Encounter

BLINK

CHESTER

Figure 1: Chester is a mobilefurniture robot. Blink, the lampon top of Chester, is his sidekick.Blink can be turned on selectivelyfor certain interactions.

Figure 2: Four participants gottheir souvenir (top), and only oneof them kept conversing with therobot (bottom).

Even in laboratory settings, unanticipated encounters withrobots can provide interesting insights into how peoplemay naturally interact with these machines. For example,we used an unanticipated encounter to study the effectsof a sidekick character in HRI [11]. In this exploratorystudy, we surprised groups of children between the ages offour and ten years with a mobile furniture robot. Therobot interacted as one or two characters simultaneously(Fig. 1), engaged in a brief social conversation with thechildren and gave them a small souvenir as a reward forcoming to the lab to test various technologies. Kids werefree to approach and touch the the platform as desiredthroughout the study.

Our findings suggested that the presence of a co-locatedsidekick character in HRI may increase attention tospoken elements of the interaction without altering users’proxemics behavior. In addition, the results of the studyreinforced earlier work that suggested that pieces offurniture can be a good robot design for children [12] andthat the anthropomorphization of household objects canproduce positive engagement effects [4].

OpportunitiesThe unexpected nature of the encounter and the lack ofparental control made the interaction naturalistic, allowingfor human behavior typical of HRI encounters in the wild,e.g., as in [2]. Some kids stood in front of the robotduring the experiment; others ran around it to see its backside. Very adventurous children tried leaning on top of theplatform or sticking their fingers in its drawers. Whenyoung kids felt insecure about the furniture robot, theytended to approach older children to interact together.

Overall, the kids’ reactions led to varied spatial behaviorduring the experiment. Children occupied different spatialzones with respect to the front of the robot, depending ontheir activity and the robot’s motion. Older kids tended toestablish spatial formations with the platform that weretypical of human-human conversations.

The children’s behavior also motivated us to use thisexperiment to explore social influence in the context ofHRI. For example, the robot said ”Ouch!” with a sad faceand ”Don’t poke me!” with an angry face to try to preventvery outgoing children from touching it in dangerousways. While these responses made some kids laugh, theyoften influenced children who empathized with the robot.Some participants even responded to the robot by tryingto control their younger peers during the interaction.

ChallengesWe recruited three to four children per session of theexperiment to interact simultaneously with our robot. Attimes, though, the interactions had fewer participantsthan we expected. For example, some children noticed therobot and approached it sooner than others at thebeginning of the experiment. After they got theirsouvenirs, kids often got distracted trying to check eachothers’ presents and ignored the robot (Fig 2). These

circumstances were often difficult to analyze andmotivated us to work on detecting social groupconversations based on spatial behavior [10]. The groupdetection method that we developed is complementary toother approaches used to recognize social engagement andenabled our robot to adapt to dynamic conversations [7].

The diffusion of responsibility phenomenon [3] seemed toaffect some children in the study. In one-on-onehuman-robot interactions, there is a clear role for eachparticipant. When the robot speaks, the user listens. Ifthe robot asks a question, the user is put on the spot toreply or the conversation stops. However, when morepeople interact, they may feel like any other person cantake the lead of the conversation. Thus, it is more sociallyacceptable for them to avoid participating. In our study,some children became simple spectators.

Responsibility diffusion can be reduced by assigning activeroles to the participants in group experiments. Forexample, we achieved this goal in two other studies whenengaging people in a brainstorming activity and in a socialrole-playing game. The next two sections provide moredetails about these experiences.

Experiment 2. Brainstorming ActivityFigure 3: Brainstorming activity(top) and status of ourperception system (bottom). Thepolygon on the ground connectsthe estimated members of therobot’s conversational group.

We designed another experiment to study group spatialbehavior further and try to reduce the diffusion ofresponsibility phenomenon. In this protocol, the robot leda brainstorming session with three to four participants in alaboratory [7]. To start the activity, the robot explainedthat the project for which it was built had ended. The onlyway to prevent the lab from retiring him was to find waysin which it could help or entertain people in the office.

The robot encouraged participants to think of how itcould be useful in the lab environment and provided some

examples on its own. For example, the robot said that itcould entertain people by telling jokes and playing games.It could also provide useful information, like the weatherforecast. In some cases, the robot encouraged participantsto provide details about their thoughts or discouragedunrealistic and complicated suggestions. When someonehad a good idea, the robot asked this person to write iton a piece of paper at a table nearby and to deposit thepaper slip in a box in the room.

Even though this experiment was run in a laboratorysetting, participants were free to move in the environmentas desired and, periodically, were induced to leave therobot’s conversational group to document their ideas.This dynamic created a variety of group formations on afrequent basis, and allowed us to test our group detectionapproach in real-time (Figure 3).

We also used this experiment to evaluate body andorientation behaviors for our robot. The results showedthat the gaze behaviors under consideration affected theparticipants’ perception of the robot’s motion. Likewise,its motion affected the perception of its gaze. Thismutual dependency suggests that robot gaze and bodymotion must be designed and controlled jointly, ratherthan independently of each other.

OpportunitiesIn comparison to the first experiment, the brainstormingactivity led to more organized interactions. Nonetheless, itwas flexible enough to allow the participants to movearound the robot as desired while keeping them engaged.Interestingly, some participants changed their pathabruptly as they were moving in the room to preventcrossing in-between conversational groups. This kind ofbehavior suggested that the use of social norms naturallyemerged during the experiment.

Because the brainstorming activity encouragescollaboration, it could be an interesting setting to studygroup dynamics and processes other than spatial behavior.For example, brainstorming sessions could be used tofurther learn about decision making, problem resolutionstrategies, social influence, or negotiation.

ChallengesThe behavior of the leader of the brainstorming activitycan have a strong effect on the interaction. Because therobot undertook this important job in our experiment, wehad to heavily test and adjust its dialog as we werepiloting the protocol. Even then, there were a fewincidents that made the participants feel ignored orexcluded by the robot unintentionally. For example, oneparticipant was ignored by the robot or misunderstoodseveral times because she spoke very quietly.Unfortunately, the lack of positive response from therobot made the participants think that it did not like her.

Figure 4: The robot playedMafia (top) or moderated thegame (bottom).

Experiment 3. A Social Role-Playing GameWe used an established social role-playing game namedMafia as another activity for group experiments [8]. Wechose this game due to its potential to reduce thediffusion of responsibility phenomenon. The game involvesplayers in group discussions to try to discover each other’ssecret roles: mafia or villagers. The mafia players hidetheir identity and try to “kill” the villagers one by one.The villagers “convict” people who they think are part ofthe mafia to try to save themselves in the game.

As in the other experiments, we recruited groups of threeor four participants to play Mafia with the robot. We letthem stand and move freely in our laboratory space toobserve the spatial arrangements that naturally emergedfrom the interaction. Our only request for the participants

was that when they got killed or convicted in Mafia, theystepped away from the remaining group. This requestfacilitated identification of the people that were stillplaying and induced group interactions of various sizes.

In one condition, the robot played the game with theparticipants. In the other condition, it was the moderatorand led the activity (Fig. 4). Several factors influencedusers’ preferences for these roles, which were reflected inratings of desired interaction time, entertainment, roleskills, the robot’s value to the game, and social inclusion.

OpportunitiesMafia is an interesting scenario to study the effects of therole of a robot in group interactions. For example, thedata from our experiment suggested that this factor couldpotentially influence human spatial behavior. Moreover, itmay be possible to accentuate a particular role bymanipulating the distance of the robot to the participants.

The underlying structure and flexibility of the game makesit a practical choice for human experiments. Rules areoften added to Mafia and modified to tailor the activity tospecific interests. For example, other efforts outside ofHRI have used the game to study human deception [5]and the effects of physical presence [1].

ChallengesPeople need to understand the rules of Mafia well to fullyengage in the game. We explained these rules in ourexperiment with a professional video that demonstratedstep by step how to play. While we expected participantsto grasp the dynamics of Mafia with this demonstration,it was hard for some of them to envision the consequencesof their actions. For example, groups often convicted therobot erroneously and made it lose the game. Yet, theywere surprised and regretted having to stop interacting

with it. Practicing the game at least once at the beginningof the experiment could help clarify game dynamics.

Experiment 4. A Reaction Game

A Deceptive Robot Referee in a Multiplayer Gaming Environment

Marynel Vázquez Robotics Institute

Carnegie Mellon University marynel@cmu.edu

Alexander May Robotics Institute

Carnegie Mellon University amay@andrew.cmu.edu

Aaron Steinfeld Robotics Institute

Carnegie Mellon University steinfeld@cmu.edu

Wei-Hsuan Chen Mechanical Engineering

Carnegie Mellon University weihsuac@andrew.cmu.edu

ABSTRACT We explore deception in the context of a multi-player robotic game. The robot does not participate as a competitor, but is in charge of declaring who wins or loses every round. The robot was designed to deceive game players by imperceptibly balancing how much they won, with the hope this behavior would make them play longer and with more interest. Inducing false belief about who wins the game was accomplished by leveraging paradigms about robot behavior and their better perceptual abilities. There were participants who found the balancing strategy favorable after being debriefed, and others who showed less interest mostly because of their perceived level of unfairness. Trust, suspicion, motivation, and appeal were evaluated by altering the robot behavior during gameplay. Post- briefing results include the finding that participants are more accepting of the use of lying by our robot as opposed to robots in general. Factors pertaining to gameplay, this robot, and deceptive robotics in general are also discussed. KEYWORDS: Human-Robot Interaction, Deception, Trust, Entertainment, Motivation 1. INTRODUCTION We typically expect robots to operate as advertised, without hidden intentions. Our attributions toward these machines are highly influenced by paradigms concerning how good and reliable electronic systems are for certain tasks in comparison to human perception. In particular, we tend to favor robots for jobs that

require memorization and keen perceptual abilities [1], thus making them valuable for judging objective results that require high precision. What if stereotypes regarding robot behavior were used to trick us? In particular, we wanted to see if human expectations for robot characteristics could be subverted to alter user perception. To do this, we studied the effect of robot deception in the context of a simple, multi-player, reflex game (Figure 1). The robot was designed to deceive game players with the hope that they would want to play longer and with more interest, while eating healthy food.

A Deceptive Robot Referee in a MultiplayerGaming Environment

Marynel Vazquez and Alexander MayRobotics Institute

Carnegie Mellon UniversityPittsburgh, PA 15213

{marynelv,amay}@andrew.cmu.edu

Aaron SteinfeldRobotics Institute

Carnegie Mellon UniversityPittsburgh, PA 15213

steinfled@cmu.edu

Wei-Hsuan ChenMechanical Engineering

Carnegie Mellon UniversityPittsburgh, PA 15213

weihsuac@andrew.cmu.edu

Abstract—We explore deception in the context of a multi-playerrobotic game. The robot does not participate as a competitor,but is in charge of declaring who wins or loses every round. Therobot was designed to deceive game players by imperceptiblybalancing how much they won, with the hope this behaviorwould make them play longer and with more interest. Inducingfalse belief about who wins the game was accomplished byleveraging paradigms about robot behavior and their betterperceptual abilities. There were participants who found thebalancing strategy favorable after being debriefed, and otherswho showed less interest mostly because of their perceived levelof unfairness. Trust, suspicion, motivation, and appeal wereevaluated by altering the robot behavior during gameplay. Post-briefing results include the finding that participants are moreaccepting of the use of lying by our robot as opposed to robots ingeneral. Factors pertaining to gameplay, this robot, and deceptiverobotics in general are also discussed.

I. INTRODUCTION

We typically expect robots to operate as advertised, withouthidden intentions. Our attributions toward these machines arehighly influenced by paradigms concerning how good andreliable electronic systems are for certain tasks in comparisonto human perception. In particular, we tend to favor robots forjobs that require memorization and keen perceptual abilities[1], thus making them valuable for judging objective resultswhich require high precision. What if stereotypes regardingrobot behavior were used to trick us?

In particular, we wanted to see if human expectations forrobot characteristics could be subverted to alter user percep-tion. To do this, we studied the effect of robot deception inthe context of a simple, multi-player, reflex game (Figure 1).The robot was designed to deceive game players with the hopethat they would want to play longer and with more interest,while eating healthy food.

Deceptive behaviors in robots raise interesting questionsconcerning morality, fault and responsibility. Reynolds andIshikawa [2] speculated ways in which robots might trick us,and discussed the role of designers and robots as “morallyresponsible” entities. Wagner and Arkin [3], [4] exploreddeception in multi-robot environments, and Short et al. [5]presented results in the context of one-to-one human-robotinteraction.

Unlike the previously cited research, our robot does notparticipate in the game as a competitor, but is in charge

Fig. 1. Experimental setting. Four players are seated close to one another,next to the robot. Each player has a controller that allows them to input theirresponse during the game. Grapes and pieces of carrots are provided (occludedby the robot in the pictures). A monitor where simple messages are printedis placed behind the robot to support its non–verbal communication.

of declaring who wins or loses. In this context, the robotcan imperceptibly balance how much players win, due to itsimplied ability to perceive faster than the users. Theoretically,the balancing behavior should increase general motivationand interest in playing due to a more balanced frequency ofwinning.

II. RELATED WORK

Short et al. [5] reported increased engagement with a robotthrough the use of deception in the context of a children’s

Figure 1. Experimental Setting

978-1-61284-639-2/11/$26.00 ©2011 IEEE 204

A Deceptive Robot Referee in a Multiplayer Gaming Environment

Marynel Vázquez Robotics Institute

Carnegie Mellon University marynel@cmu.edu

Alexander May Robotics Institute

Carnegie Mellon University amay@andrew.cmu.edu

Aaron Steinfeld Robotics Institute

Carnegie Mellon University steinfeld@cmu.edu

Wei-Hsuan Chen Mechanical Engineering

Carnegie Mellon University weihsuac@andrew.cmu.edu

ABSTRACT We explore deception in the context of a multi-player robotic game. The robot does not participate as a competitor, but is in charge of declaring who wins or loses every round. The robot was designed to deceive game players by imperceptibly balancing how much they won, with the hope this behavior would make them play longer and with more interest. Inducing false belief about who wins the game was accomplished by leveraging paradigms about robot behavior and their better perceptual abilities. There were participants who found the balancing strategy favorable after being debriefed, and others who showed less interest mostly because of their perceived level of unfairness. Trust, suspicion, motivation, and appeal were evaluated by altering the robot behavior during gameplay. Post- briefing results include the finding that participants are more accepting of the use of lying by our robot as opposed to robots in general. Factors pertaining to gameplay, this robot, and deceptive robotics in general are also discussed. KEYWORDS: Human-Robot Interaction, Deception, Trust, Entertainment, Motivation 1. INTRODUCTION We typically expect robots to operate as advertised, without hidden intentions. Our attributions toward these machines are highly influenced by paradigms concerning how good and reliable electronic systems are for certain tasks in comparison to human perception. In particular, we tend to favor robots for jobs that

require memorization and keen perceptual abilities [1], thus making them valuable for judging objective results that require high precision. What if stereotypes regarding robot behavior were used to trick us? In particular, we wanted to see if human expectations for robot characteristics could be subverted to alter user perception. To do this, we studied the effect of robot deception in the context of a simple, multi-player, reflex game (Figure 1). The robot was designed to deceive game players with the hope that they would want to play longer and with more interest, while eating healthy food.

A Deceptive Robot Referee in a MultiplayerGaming Environment

Marynel Vazquez and Alexander MayRobotics Institute

Carnegie Mellon UniversityPittsburgh, PA 15213

{marynelv,amay}@andrew.cmu.edu

Aaron SteinfeldRobotics Institute

Carnegie Mellon UniversityPittsburgh, PA 15213

steinfled@cmu.edu

Wei-Hsuan ChenMechanical Engineering

Carnegie Mellon UniversityPittsburgh, PA 15213

weihsuac@andrew.cmu.edu

Abstract—We explore deception in the context of a multi-playerrobotic game. The robot does not participate as a competitor,but is in charge of declaring who wins or loses every round. Therobot was designed to deceive game players by imperceptiblybalancing how much they won, with the hope this behaviorwould make them play longer and with more interest. Inducingfalse belief about who wins the game was accomplished byleveraging paradigms about robot behavior and their betterperceptual abilities. There were participants who found thebalancing strategy favorable after being debriefed, and otherswho showed less interest mostly because of their perceived levelof unfairness. Trust, suspicion, motivation, and appeal wereevaluated by altering the robot behavior during gameplay. Post-briefing results include the finding that participants are moreaccepting of the use of lying by our robot as opposed to robots ingeneral. Factors pertaining to gameplay, this robot, and deceptiverobotics in general are also discussed.

I. INTRODUCTION

We typically expect robots to operate as advertised, withouthidden intentions. Our attributions toward these machines arehighly influenced by paradigms concerning how good andreliable electronic systems are for certain tasks in comparisonto human perception. In particular, we tend to favor robots forjobs that require memorization and keen perceptual abilities[1], thus making them valuable for judging objective resultswhich require high precision. What if stereotypes regardingrobot behavior were used to trick us?

In particular, we wanted to see if human expectations forrobot characteristics could be subverted to alter user percep-tion. To do this, we studied the effect of robot deception inthe context of a simple, multi-player, reflex game (Figure 1).The robot was designed to deceive game players with the hopethat they would want to play longer and with more interest,while eating healthy food.

Deceptive behaviors in robots raise interesting questionsconcerning morality, fault and responsibility. Reynolds andIshikawa [2] speculated ways in which robots might trick us,and discussed the role of designers and robots as “morallyresponsible” entities. Wagner and Arkin [3], [4] exploreddeception in multi-robot environments, and Short et al. [5]presented results in the context of one-to-one human-robotinteraction.

Unlike the previously cited research, our robot does notparticipate in the game as a competitor, but is in charge

Fig. 1. Experimental setting. Four players are seated close to one another,next to the robot. Each player has a controller that allows them to input theirresponse during the game. Grapes and pieces of carrots are provided (occludedby the robot in the pictures). A monitor where simple messages are printedis placed behind the robot to support its non–verbal communication.

of declaring who wins or loses. In this context, the robotcan imperceptibly balance how much players win, due to itsimplied ability to perceive faster than the users. Theoretically,the balancing behavior should increase general motivationand interest in playing due to a more balanced frequency ofwinning.

II. RELATED WORK

Short et al. [5] reported increased engagement with a robotthrough the use of deception in the context of a children’s

Figure 1. Experimental Setting

978-1-61284-639-2/11/$26.00 ©2011 IEEE 204

Figure 5: Groups of participantsplaying ShakeTime! in ourexperiment.

CORKSCREWVIBRATINGFRUIT

CONTROLLER

Figure 6: Main components ofthe robot that we built forShakeTime!

We conducted a more controlled experiment to studyengagement and robot deception in HRI [9]. For thisprotocol, we designed a multi-player reaction game namedShakeTime! (Fig. 5). The main body of the robot used inthe game consisted of a turntable, concealing electroniccomponents from players (Fig. 6). The turntable heldvibrating plastic fruits and a quasianthropomorphiccorkscrew that acted as a referee figure in the game. Theturntable was placed in front of a monitor during theexperiment to provide visual support to human-robotcommunication and to accelerate the rate at which playerslearned the mechanics of the game.

The main objective during a round of ShakeTime! wasdetecting the vibration of a specific “target” fruit. Thefirst of four players to react by pressing a button was thesupposed winner. Winners ate grapes as a reward, whilethose who lost consumed small pieces of carrot.

Whenever possible, the robot tried to balance winningbetween participants by subverting human expectations forrobot characteristics. If a group of players reacted to thetarget within a short window of time, the robot declaredas the winner whoever had lost the most within the group.

OpportunitiesSimilar to Mafia, ShakeTime! is a competitive game withflexible rules. In our experiment, we used the game todemonstrate that it is possible to co-opt stereotypes aboutrobot behavior in multi-party interactions. Interestingly,participants’ ratings of suspicion were correlated withfeelings that the robot was malfunctioning even when therobot altered the outcome of the game noticeably.

Our study reinforced the idea that deception may notnegatively impact users’ willingness to interact withrobots [6]. Nonetheless, we doubt deception will beequally accepted in different circumstances. Participants’responses showed more acceptance of lying behavior fromour robot compared to robots in general, suggesting thatrobots for entertainment will be given more room to lie.

ChallengesParticipants generally behaved as expected, but theirreactions to the game sometimes reduced engagementlevels with the activity or influenced their interaction. Forexample, some participants got distracted when theystarted to discuss ways to figure out the robot’s behavior.A player that did not want to eat more carrots also triedto influence the other participants in the group to let himwin and eat grapes. This player asked people to let himreact first to the target fruit “in the spirit of the team”,even though they were not playing together butcompeting against each other. While we do not think thatthese kind of situations negatively impacted ourexperiment, they can potentially alter the outcome of thegame and group interactions.

DiscussionThis paper described four experimental protocols that weused to study groups in the context of HRI. Even thoughsome protocols are more controlled than others, they allinvolve participants in social interactions and can lead tothe emergence of interesting group phenomena. Theprotocols provide good opportunities to investigateengagement, turn-taking patterns, and social influence.Furthermore, the first three experiments can be used tostudy human spatial behavior with and around robots.

Participant recruitment can be a common challenge inconducting group experiments like those we described. In

our experience, recruitment can be facilitated by allowingpeople to participate in experiments with friends oracquaintances. In these cases, it is important to considerthe familiarity between the participants as a possibleconfounding factor in data analysis.

Even though our experiments were conducted in thelaboratory and were limited in several ways, we hope thatthey can be used by the community to further study HRIand, ultimately, help robots better interact in the wild. Weplan to continue working on advancing our understandingof group interactions and developing technology to allowthese machines to deal with complicated multi-partysocial dynamics. We expect these efforts will not only helprobots better interact with many people, but also makethem more socially capable in one-on-one encounters.

AcknowledgementsSupport for the first three experiments described in thispaper was provided by Disney Research. The NationalScience Foundation partially supported the lastexperiment under Grant No. IIS-0905148.

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