Synchrony and perception in robotic imitation across embodiments

Abstract

Social robotics opens up the possibility of individualized social intelligence in member robots of a community, and allows us to harness not only individual learning by the individual robot, but also the acquisition of new skills by observing other members of the community (robot, human, or virtual). We describe ALICE (Action Learning for Imitation via Correspondences between Embodiments), an implemented generic mechanism for solving the correspondence problem between differently embodied robots. ALICE enables a robotic agent to learn a behavioral repertoire suitable to performing a task by observing a model agent, possibly having a different type of body, joints, different number of degrees of freedom, etc. Previously we demonstrated that the character of imitation achieved will depend on the granularity of subgoal matching, and on the metrics used to evaluate success. In this work, we implement ALICE for simple robotic arm agents in simulation using various metrics for evaluating success according to actions, states, or effects or weighted combinations. We examine the roles of synchronization, looseness of perceptual match, and of proprioceptive matching by a series of experiments. As a complement to the social developmental aspects suggested by developmental psychology, our results show that synchronization and loose perceptual matching also allow for faster acquisition of behavioral competencies at low error rates. We also discuss the use of social learning mechanisms like ALICE for transmission of skills between robots, and give the first example of transmission of a skill through a chain of robots, despite differences in embodiment of agents involved. This simple example demonstrates that by using social learning and imitation, cultural transmission is possible among robots, even heterogeneous groups of robots.