Expressive social exchange between humans and robots

Thesis (Sc.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (p. 253-264).Sociable humanoid robots are natural and intuitive for people to communicate with and to teach. We present recent advances in building an autonomous humanoid robot, Kismet, that can engage humans in expressive social interaction. We outline a set of design issues and a framework that we have found to be of particular importance for sociable robots. Having a human-in-the-loop places significant social constraints on how the robot aesthetically appears, how its sensors are configured, its quality of movement, and its behavior. Inspired by infant social development, psychology, ethology, and evolutionary perspectives, this work integrates theories and concepts from these diverse viewpoints to enable Kismet to enter into natural and intuitive social interaction with a human caregiver, reminiscent of parent-infant exchanges. Kismet perceives a variety of natural social cues from visual and auditory channels, and delivers social signals to people through gaze direction, facial expression, body posture, and vocalizations. We present the implementation of Kismet's social competencies and evaluate each with respect to: 1) the ability of naive subjects to read and interpret the robot's social cues, 2) the robot's ability to perceive and appropriately respond to naturally offered social cues, 3) the robot's ability to elicit interaction scenarios that afford rich learning potential, and 4) how this produces a rich, flexible, dynamic interaction that is physical, affective, and social. Numerous studies with naive human subjects are described that provide the data upon which we base our evaluations.by Cynthia L. Breazeal.Sc.D

Modeling the Dynamics of Nonverbal Behavior on Interpersonal Trust for Human-Robot Interactions

We describe research towards creating a computational model for recognizing interpersonal trust in social interactions. We found that four negative gestural cues—leaning-backward, face-touching, hand-touching, and crossing-arms—are together predictive of lower levels of trust. Three positive gestural cues—leaning-forward, having arms-in-lap, and open-arms—are predictive of higher levels of trust. We train a probabilistic graphical model using natural social interaction data, a “Trust Hidden Markov Model” that incorporates the occurrence of these seven important gestures throughout the social interaction. This Trust HMM predicts with 69.44% accuracy whether an individual is willing to behave cooperatively or uncooperatively with their novel partner; in comparison, a gesture-ignorant model achieves 63.89% accuracy. We attempt to automate this recognition process by detecting those trust-related behaviors through 3D motion capture technology and gesture recognition algorithms. We aim to eventually create a hierarchical system—with low-level gesture recognition for high-level trust recognition—that is capable of predicting whether an individual finds another to be a trustworthy or untrustworthy partner through their nonverbal expressions

Mobile Devices for Early Literacy Intervention and Research with Global Reach

Extensive work focuses on the uses of technology at scale for post-literate populations (e.g., MOOC, Learning Games, LMS’s). Little attention is afforded to non-literate populations, particularly in the developing world. This paper presents an approach using mobile devices with the ultimate goal to reach 770 million people. We developed a novel platform with a cloud backend to deliver educational content to over a thousand marginalized children in different countries:specifically, in remote villages without schools, urban slums with overcrowded schools, and at-risk, rural schools. Here we describe the theoretical basis of our system and results from case studies in three educational contexts. This model will help researchers and designers understand how mobile devices can help children acquire basic skills and aid each other’s learning when the benefit of teachers is limited or non-existent.Italian Development CouncilMRP FoundationRoanoke County School

MeBot: A robotic platform for socially embodied telepresence

Telepresence refers to a set of technologies that allow users to feel present at a distant location; telerobotics is a subfield of telepresence. This paper presents the design and evaluation of a telepresence robot which allows for social expression. Our hypothesis is that a telerobot that communicates more than simply audio or video but also expressive gestures, body pose and proxemics, will allow for a more engaging and enjoyable interaction. An iterative design process of the MeBot platform is described in detail, as well as the design of supporting systems and various control interfaces. We conducted a human subject study where the effects of expressivity were measured. Our results show that a socially expressive robot was found to be more engaging and likable than a static one. It was also found that expressiveness contributes to more psychological involvement and better cooperation.Digital LifeThings That Think Consortiui

Storytelling with robots: Learning companions for preschool children's language development

Children's oral language skills in preschool can predict their academic success later in life. As such, increasing children's skills early on could improve their success in middle and high school. To this end, we propose that a robotic learning companion could supplement children's early language education. The robot targets both the social nature of language learning and the adaptation necessary to help individual children. The robot is designed as a social character that interacts with children as a peer, not as a tutor or teacher. It will play a storytelling game, during which it will introduce new vocabulary words, and model good story narration skills, such as including a beginning, middle, and end; varying sentence structure; and keeping cohesion across the story. We will evaluate whether adapting the robot's level of language to the child's - so that, as children improve their storytelling skills, so does the robot - influences (i) whether children learn new words from the robot, (ii) the complexity and style of stories children tell, (iii) the similarity of children's stories to the robot's stories. We expect children will learn more from a robot that adapts to maintain an equal or greater ability than the children, and that they will copy its stories and narration style more than they would with a robot that does not adapt (a robot of lesser ability). However, we also expect that playing with a robot of lesser ability could prompt teaching or mentoring behavior from children, which could also be beneficial to language learning.National Science Foundation (U.S.) (NSF Grant 122886)National Science Foundation (U.S.) (NSF Grant CCF-1138986)National Science Foundation (U.S.) (NSF Graduate Research Fellowship, grant number 1122374

A Long-Term Study of Young Children's Rapport, Social Emulation, and Language Learning With a Peer-Like Robot Playmate in Preschool

Prior research has demonstrated the importance of children's peers for their learning and development. In particular, peer interaction, especially with more advanced peers, can enhance preschool children's language growth. In this paper, we explore one factor that may modulate children's language learning with a peer-like social robot: rapport. We explore connections between preschool children's learning, rapport, and emulation of the robot's language during a storytelling intervention. We performed a long-term field study in a preschool with 17 children aged 4–6 years. Children played a storytelling game with a social robot for 8 sessions over two months. For some children, the robot matched the level of its stories to the children's language ability, acting as a slightly more advanced peer (Matched condition); for the others, the robot did not match the story level (Unmatched condition). We examined children's use of target vocabulary words and key phrases used by the robot, children's emulation of the robot's stories during their own storytelling, and children's language style matching (LSM—a measure of overlap in function word use and speaking style associated with rapport and relationship) to see whether they mirrored the robot more over time. We found that not only did children emulate the robot more over time, but also, children who emulated more of the robot's phrases during storytelling scored higher on the vocabulary posttest. Children with higher LSM scores were more likely to emulate the robot's content words in their stories. Furthermore, the robot's personalization in the Matched condition led to increases in both children's emulation and their LSM scores. Together, these results suggest first, that interacting with a more advanced peer is beneficial for children, and second, that children's emulation of the robot's language may be related to their rapport and their learning. This is the first study to empirically support that rapport may be a modulating factor in children's peer learning, and furthermore, that a social robot can serve as an effective intervention for language development by leveraging this insight. Keyword: Children; Language development; Mimicry; Peer modeling; Rapport; Relationship; Social robotics; StorytellingNational Science Foundation (U.S.) (Grant 122886)National Science Foundation (U.S.) (Grant CCF-1138986)National Science Foundation (U.S.). Graduate Research Fellowship Program (Grant 1122374

Persuasive Robotics: the influence of robot gender on human behavior

Persuasive Robotics is the study of persuasion as it applies to human-robot interaction (HRI). Persuasion can be generally defined as an attempt to change another’s beliefs or behavior. The act of influencing others is fundamental to nearly every type of social interaction. Any agent desiring to seamlessly operate in a social manner will need to incorporate this type of core human behavior. As in human interaction, myriad aspects of a humanoid robot’s appearance and behavior can significantly alter its persuasiveness – this work will focus on one particular factor: gender. In the current study, run at the Museum of Science in Boston, subjects interacted with a humanoid robot whose gender was varied. After a short interaction and persuasive appeal, subjects responded to a donation request made by the robot, and subsequently completed a post-study questionnaire. Findings showed that men were more likely to donate money to the female robot, while women showed little preference. Subjects also tended to rate the robot of the opposite sex as more credible, trustworthy, and engaging. In the case of trust and engagement the effect was much stronger between male subjects and the female robot. These results demonstrate the importance of considering robot and human gender in the design of HRI.Massachusetts Institute of Technology. Media Laborator

Social Robots as Creativity Eliciting Agents

Can robots help children be more creative? In this work, we posit social robots as creativity support tools for children in collaborative interactions. Children learn creative expressions and behaviors through social interactions with others during playful and collaborative tasks, and socially emulate their peers’ and teachers’ creativity. Social robots have a unique ability to engage in social and emotional interactions with children that can be leveraged to foster creative expression. We focus on two types of social interactions: creativity demonstration, where the robot exhibits creative behaviors, and creativity scaffolding, where the robot poses challenges, suggests ideas, provides positive reinforcement, and asks questions to scaffold children’s creativity. We situate our research in three playful and collaborative tasks - the Droodle Creativity game (that affords verbal creativity), the MagicDraw game (that affords figural creativity), and the WeDo construction task (that affords constructional creativity), that children play with Jibo, a social robot. To evaluate the efficacy of the robot’s social behaviors in enhancing creative behavior and expression in children, we ran three randomized controlled trials with 169 children in the 5–10 yr old age group. In the first two tasks, the robot exhibited creativity demonstration behaviors. We found that children who interacted with the robot exhibiting high verbal creativity in the Droodle game and high figural creativity in the MagicDraw game also exhibited significantly higher creativity than a control group of participants who interacted with a robot that did not express creativity (p < 0.05*). In the WeDo construction task, children who interacted with the robot that expressed creative scaffolding behaviors (asking reflective questions, generating ideas and challenges, and providing positive reinforcement) demonstrated higher creativity than participants in the control group by expressing a greater number of ideas, more original ideas, and more varied use of available materials (p < 0.05*). We found that both creativity demonstration and creativity scaffolding can be leveraged as social mechanisms for eliciting creativity in children using a social robot. From our findings, we suggest design guidelines for pedagogical tools and social agent interactions to better support children’s creativity

Telling Stories to Robots: The Effect of Backchanneling on a Child's Storytelling

While there has been a growing body of work in child-robot interaction, we still have very little knowledge regarding young children's speaking and listening dynamics and how a robot companion should decode these behaviors and encode its own in a way children can understand. In developing a backchannel prediction model based on observed nonverbal behaviors of 4-6 year-old children, we investigate the effects of an attentive listening robot on a child's storytelling. We provide an extensive analysis of young children's nonverbal behavior with respect to how they encode and decode listener responses and speaker cues. Through a collected video corpus of peer-to-peer storytelling interactions, we identify attention-related listener behaviors as well as speaker cues that prompt opportunities for listener backchannels. Based on our findings, we developed a backchannel opportunity prediction (BOP) model that detects four main speaker cue events based on prosodic features in a child's speech. This rule-based model is capable of accurately predicting backchanneling opportunities in our corpora. We further evaluate this model in a human-subjects experiment where children told stories to an audience of two robots, each with a different backchanneling strategy. We find that our BOP model produces contingent backchannel responses that conveys an increased perception of an attentive listener, and children prefer telling stories to the BOP model robot.National Science Foundation (U.S.) (NSF grant IIS-1523118

Growing Growth Mindset with a Social Robot Peer

Mindset has been shown to have a large impact on people's academic, social, and work achievements. A growth mindset, i.e., the belief that success comes from effort and perseverance, is a better indicator of higher achievements as compared to a fixed mindset, i.e., the belief that things are set and cannot be changed. Interventions aimed at promoting a growth mindset in children range from teaching about the brain's ability to learn and change, to playing computer games that grant brain points for effort rather than success. This work explores a novel paradigm to foster a growth mindset in young children where they play a puzzle solving game with a peer-like social robot. The social robot is fully autonomous and programmed with behaviors suggestive of it having either a growth mindset or a neutral mindset as it plays puzzle games with the child. We measure the mindset of children before and after interacting with the peer-like robot, in addition to measuring their problem solving behavior when faced with a challenging puzzle. We found that children who played with a growth-mindset robot 1) self-reported having a stronger growth mindset and 2) tried harder during a challenging task, as compared to children who played with the neutral-mindset robot. These results suggest that interacting with peer-like social robot with a growth mindset can promote the same mindset in children. Keywords: early childhood education; mindset; perseverance; grit; child-robot interaction; cognitive architecture; social robotsNational Institute of Health (Grant 5R01HD086899–02