Children naturally engage in pretend play with others in their daily life. The children share each other’s expectations toward objects and people around them. Such expectation-sharing can be considered the fundamental process of social communication, where we exchange beliefs, desires, and intentions. Firstly, the present paper examines some cases of such expectation-sharing in pretend play observed in our longitudinal robot-mediated interaction with 27 preschoolers (3-to-4-year-olds) at a kindergarten. The robot, which the researchers in another room remotely controlled, has a simple appearance and motion to accept various expectations from the children. The robot functioned as a pivot for the children to exchange each other’s expectations towards the robot, toys, and people. The children might bring expectations incongruent with each other, but such expectations gradually converged into a consistent one, on which they understood each other’s verbal and non-verbal actions. Secondly, this paper interprets and models these cases using the predictive coding theory. According to the theory, the children project their predictions onto the shared environment and update the predictions by minimizing the error against reality. This act of projection, which was performed as labeling (e.g., a block as a rice ball) or pretend action (e.g., feeding it to the robot) by the children, is a form of active inference to modify the environment to match the prediction. The children exchanged and coordinated their predictions through visible action collectively accumulated in the playing environment around the robot. We conclude that joint prediction is the fundamental process of social communication, where we coordinate each other’s behavior for collaboration.

Over the years, robots have been developed to help humans in their everyday life, from preparing food, to autism therapy [2]. To accomplish their tasks, in addition to their engineered skills, today's robots are now learning from observing humans, from interacting with them [1]. Therefore, one may expect that one day, robots may develop a form of consciousness, and a desire for freedom. Hopefully, this desire will come with a wish for robots, to become an integral part of our human society.

Keepon is a robot designed for social interaction with children for the purposes of social development research and autism therapy [1]. Keepon's capacity for rhythmic synchrony in the form of dance has resulted in the popularity of several fictional music videos on the internet [2,3]. During a research collaboration visit at the KAIST PES Design Lab in Korea, Keepon's creators added this new chapter to the story of Keepon's travels. Upon watching a video of traditional Korean "Pungmulnori" dancing, which features distinctive spinning hats, Keepon becomes enamored. The robot has many adventures as he travels around Korea in search of a dance group that finally welcomes him into their cultural performance.

We propose a model of evolutionary interaction between two robots where signs used for communication emerge through mutual adaptation. Signs used in human interaction, e.g., language, gestures and eye contact change and evolve in form and meaning through repeated use. To create flexible human-like interaction systems, it is necessary to deal with signs as a dynamic property and to construct a framework in which signs emerge from mutual adaptation by agents. Our target is multi-modal interaction using voice and motion between two robots where a voice/motion pattern is used as a sign referring to a motion/voice pattern. To enable evolutionary signs (voice and motion patterns) to be recognized and generated, we utilized a dynamics model: Multiple Timescale Recurrent Neural Network (MTRNN). To enable the robots to interpret signs, we utilized hierarchical neural networks, which transform dynamics model parameters of voice/motion into those of motion/voice. In our experiment, two robots modified their own interpretation of signs constantly through mutual adaptation in interaction where they responded to the other's voice with motion one after the other. As a result of the experiment, we found that the interaction kept evolving through the robots' repeated and alternate miscommunications and re-adaptations, and this induced the emergence of diverse new signs that depended on the robots' body dynamics through the generalization capability of MTRNN.

Human social behavior is rhythmic, and synchrony plays an important role in coordinating and regulating our interactions. We are developing technology that allows the robot Keepon to perceive and behave rhythmically, and to synchronize its dancing behaviors to music or to children's movement as perceived using pressure sensors. We present two experiments in which Keepon dances with children to music, and in which the robot's rhythmic attention and role of leader or follower are manipulated in order to examine the effects on engagement and rhythmic synchrony. We found that children can assume the roles of leader or follower in a rhythmic interaction, that followers indeed tend to synchronize with the robot's movements, and that the role of follower causes the children to more closely follow a musical rhythm.

This paper describes a robotic system that uses dance as a form of social interaction to explore the properties and importance of rhythmic movement in general social interaction. The system consists of a small creature-like robot whose movement is controlled by a rhythm-based software system. Environmental rhythms can be extracted from auditory or visual sensory stimuli, and the robot synchronizes its movement to a dominant rhythm. The system was demonstrated, and an exploratory study conducted, with children interacting with the robot in a generalized dance task. Through a behavioral analysis of videotaped interactions, we found that the robot's synchronization with the background music had an effect on children's interactive involvement with the robot. Furthermore, we observed a number of expected and unexpected styles and modalities of interactive exploration and play that inform our discussion on the next steps in the design of a socially rhythmic robotic system.

We present here a progress report of our ongoing observation [2] of a group of preschoolers at three to four years of age interacting with an interactive robot. We placed the robot in their playroom and tele-controlled it from a remote room. The children showed not only individual actions, such as approach to, exploration of, and interaction with the robot, but also collective social actions, where the children spontaneously and actively situate the robot in their circle of, for example, playing house. We assume that the robot's simple appearance and comprehensive behavior derived (1) caretaking behavior from the children and (2) exchanging of such behavior among children, both of which would be important in the development of interpersonal communication and in pedagogical interventions to it.

This paper reports our longitudinal observation of unconstrained child-robot interaction at a daycare center for autistic children. We used a simple robot,Keepon, that is only capable of expressing its attention (directing its gaze) and emotions (pleasure and excitement). While controlled by a remote experimenter,Keeponinteracted with the children with its simple appearance and actions. With a sense of curiosity and security, the children spontaneously approachedKeeponand engaged in dyadic interaction with it, which then extended to triadic interactions where they exchanged with adult caregivers pleasure and surprise found inKeepon. The three-year-long observation suggests that autistic children possess the motivation to share mental states with others, which is contrary to the commonly held position that the motivation is impaired in autism. Autistic children, however, have difficulty rather in sifting out socially meaningful information (e.g., attention and emotions) from flooding perceptual information.Keepon, with its minimal expressiveness, directly conveyed socially meaningful information to the children, which activated their intact motivation to share interests and feelings with others. We conclude that social filtering is one of the prerequisites for interpersonal communication and that robots likeKeeponcan facilitate social filtering and its development in autistic children.

We are currently investigating the use of rhythm and synchrony in human-robot interaction. Specifically, we are developing techniques for the perception and generation of social rhythmic behaviors in nonverbal dance-oriented play between children and a small creature-like robot. While our goal is to develop and evaluate technological artifacts that can participate in rhythmically coordinated social interactions, it is first necessary to develop and evaluate appropriate methodologies for the evaluation of such technology. In this paper we present our experience in developing and revising an experimental paradigm for the evaluation of dance-oriented play with a robot. We propose that strictly controlled experimental paradigms for even such constrained interactions preclude the full expression (and therefore observation) of interactive behaviors that can be considered sufficiently rich and natural for the study of the complex yet fundamental rhythmic properties of social interaction.

This paper proposes a new indicator of text structure, called the lexical cohesion profile (LCP), which locates segment boundaries in a text. A text segment is a coherent scene; the words in a segment are linked together via lexical cohesion relations. LCP records mutual similarity of words in a sequence of text. The similarity of words, which represents their cohesiveness, is computed using a semantic network. Comparison with the text segments marked by a number of subjects shows that LCP closely correlates with the human judgments. LCP may provide valuable information for resolving anaphora and ellipsis.