Interpersonal Synchrony: A Survey of Evaluation Methods across Disciplines <s> Prospects in Developmental Robotics <s> Human learning is distinguished by the range and complexity of skills that can be learned and the degree of abstraction that can be achieved compared with those of other species. Homo sapiens is also the only species that has developed formal ways to enhance learning: teachers, schools, and curricula. Human infants have an intense interest in people and their behavior and possess powerful implicit learning mechanisms that are affected by social interaction. Neuroscientists are beginning to understand the brain mechanisms underlying learning and how shared brain systems for perception and action support social learning. Machine learning algorithms are being developed that allow robots and computers to learn autonomously. New insights from many different fields are converging to create a new science of learning that may transform educational practices. <s> BIB001 </s> Interpersonal Synchrony: A Survey of Evaluation Methods across Disciplines <s> Prospects in Developmental Robotics <s> This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic, and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: 1) how agents learn and represent compositional actions; 2) how agents learn and represent compositional lexica; 3) the dynamics of social interaction and learning; and 4) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics. <s> BIB002 </s> Interpersonal Synchrony: A Survey of Evaluation Methods across Disciplines <s> Prospects in Developmental Robotics <s> Synchrony is claimed by psychology as a crucial parameter of any social interaction: to give to human a feeling of natural interaction, a feeling of agency [17], an agent must be able to synchronise with this human on appropriate time [29] [11] [15] [16] [27]. In the following experiment, we show that synchrony can be more than a state to reach during interaction, it can be a useable cue of the human’s satisfaction and level of engagement concerning the ongoing interaction: the better is the interaction, the more synchronous with the agent is the human. We built an architecture that can acquire a human partner’s level of synchrony and use this parameter to adapt the agent behavior. This architecture detects temporal relation [1] existing between the actions of the agent and the actions of the human. We used this detected level of synchrony as reinforcement for learning [6]: the more constant the temporal relation between agent and human remains, the more positive is the reinforcement, conversely if the temporal relation varies above a threshold the reinforcement is negative. In a teaching task, this architecture enables naive humans to make the agent learn left-right associations just by the mean of intuitive interactions. The convergence of this learning reinforced by synchrony shows that synchrony conveys current information concerning human satisfaction and that we are able to extract and reuse this information to adapt the agent behavior appropriately. <s> BIB003
In the last decade, researchers in the field of robotics, signal processing, and artificial intelligence have taken a growing interest in developmental phenomena, such as parent-infant synchrony, language acquisition, and joint attention BIB001 . For instance, the goal of developmental robotics is to enable robots and other artificial systems to autonomously develop skills for any particular environment rather than programming them to solve particular goals for a specific environment. This approach was inspired by human-infant interaction to design robots. Cangelosi et al. BIB002 identified a sequence of milestones for future research in the field. In the social learning section, the first target involves studying and implementing nonverbal social cues for language and skill learning. By decoding the appropriate nonverbal signals, robots can achieve joint attention with the partner, orient their gaze toward the partner's focus of attention, and mirror the partner, among other things. Through these synchronized exchanges, the robot can acquire language by associating its focus of attention with information extracted from the partner's speech. Prepin and Gaussier BIB003 proposed a robotic architecture (ADRIANA) able to measure the degree of synchrony with a human and adapt its behavior accordingly. The robot is equipped with two arms and two possible positions (lowered or raised). Participants were asked to "make the robot learn to move the arms, which is on the same side as the one they move." At the beginning of the experiment, the robot raises or lowers one of its arms, randomly left or right, when the human does. At each trial, the time delay between the robot and the human is predicted according to the past observations. Synchrony is assessed as the error between the predicted delay and the real delay. Thus, synchrony is used as a reinforcement signal to learn right-left associations. They found that the learning process converged for all participants.