This paper addresses the role of imitation as a means to enhance the learning of communication skills in autonomous robots. A series of robotic experiments are presented in which autonomous mobile robots are taught a synthetic proto-language. Learning of the language occurs through an imitative scenario where the robot replicates the teacher's movements. Imitation is here an implicit attentional mechanism which allows the robot imitator to share a similar set of proprio- and exteroceptions with the teacher. The robot grounds its understanding of the teacher's words, which describe the teacher's current observations, upon its own perceptions which are similar to those of the teacher. Learning of the robot is based on a dynamical recurrent associative memory architecture (DRAMA). Learning is unsupervised and results from the self-organization of the robot's connectionist architecture. Results show that the imitative behavior greatly improves the efficiency and speed of the learning. More...

This article presents a biologically inspired model for motor skills imitation. The model is composed of modules whose functinalities are inspired by corresponding brain regions responsible for the control of movement in primates. These modules are high-level abstractions of the spinal cord, the primary and premotor cortexes (M1 and PM), the cerebellum, and the temporal cortex. Each module is modeled at a connectionist level. Neurons in PM respond both to visual observation of movements and to corresponding motor commands produced by the cerebellum. As such, they give an abstract representation of mirror neurons. Learning of new combinations of movements is done in PM and in the cerebellum. Premotor cortexes and cerebellum are modeled by the DRAMA neural architecture which allows learning of times series and of spatio-temporal invariance in multimodal inputs. The model is implemented in a mechanical simulation of two humanoid avatars, the imitator and the imitatee. Three types of sequences learning are presented: (1) learning of repetitive patterns of arm and leg movements; (2) learning of oscillatory movements of shoulders and elbows, using video data of a human demonstration; 3) learning of precise movements of the extremities for grasp and reach

. Increasingly socially intelligent agents (software or robotic) are used in education, rehabilitation and therapy. This paper discusses the role of interactive, mobile robots as social mediators in the particular domain of autism therapy. This research is part of the project AURORA that studies how mobile robots can be used to teach children with autism basic interaction skills that are important in social interactions among humans. Results from a particular series of trials involving pairs of two children and a mobile robot are described. The results show that the scenario with pairs of children and a robot creates a very interesting social context which gives rise to a variety of different social and non-social interaction patterns, demonstrating the specific problems but also abilities of children with autism in social interactions. Future work will include a closer analysis of interactional structure in human-human and robot-human interaction. We outline a particular framework that we are investigating. 1

Introduction This chapter presents the agent-based perspective on imitation. In this perspective, imitation is best considered as the behavior of an autonomous agent in relation to its environment, including other autonomous agents. We argue that such a perspective helps unfold the full potential of research on imitation and helps in identifying challenging and important research issues. We first explain the agent-based perspective and then discuss it in the context of particular research issues in studies with animals and artifacts, with reference to chapters presented in this book. At the end of the chapter we briefly introduce the individual contributions to this book and provide a roadmap that helps the reader in navigating through the exciting and highly interwoven themes that are presented in this book. In order to focus discussions, we explain the agent-based perspective with particular consideration of the correspondence

This paper addresses the problem of body mapping in robotic imitation where the demonstrator and imitator may not share the same embodiment [degrees of freedom (DOFs), body morphology, constraints, affordances, and so on]. Body mappings are formalized using a unified (linear) approach via correspondence matrices, which allow one to capture partial, mirror symmetric, one-to-one, one-to-many, many-to-one, and many-to-many associations between various DOFs across dissimilar embodiments. We show how metrics for matching state and action aspects of behavior can be mathematically determined by such correspondence mappings, which may serve to guide a robotic imitator. The approach is illustrated and validated in a number of simulated 3-D robotic examples, using agents described by simple kinematic models and different types of correspondence mappings.

This article discusses narrative intelligence in the context of the evolution of primate (social) intelligence, and with respect to the particular cognitive limits that constrain the development of human social networks and societies.

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

This paper focuses on our works on imitation in autonomous robots. In a first part, we take into account recent studies in the field of developmental psychology and consider the two functions of imitation (learning and communication) that these studies have stressed. In a second part, we propose the idea that a proto imitative behavior can be induced in a mobile robot via a limitation of its visual perception. We provide a robotic implementation in which a mobile robot recognizes its teacher's image. Finally, we discuss imitation in a non-supervised context and we propose a simple system which learns motor associations with non explicit reinforcement on the basis of its ability to detect novelty. 1.

This paper presents results of a novel study on interaction kinesics where 18 children interacted with a humanoid child-sized robot called KASPAR. Based on findings in psychology and social sciences we propose the temporal behaviour matching hypothesis which predicts that children will adapt to and match the robot’s temporal behaviour. Each child took part in six experimental trials involving two games in which the dynamics of interactions played a key part: a body expression imitation game, where the robot imitated expressions demonstrated by the children, and a drumming game where the robot mirrored the children’s drumming. In both games KASPAR responded either with or without a delay. Additionally, in the drumming game, KASPAR responded with or without exhibiting facial/gestural expressions. Individual case studies as well as statistical analysis of the complete sample are presented. Results show that a delay of the robot’s drumming response lead to larger pauses (with and without robot nonverbal gestural expressions) and longer drumming durations (with nonverbal gestural expressions only). In the imitation game, the robot’s delay lead to longer imitation eliciting behaviour with longer pauses for the children, but systematic individual differences are observed in regards to the effects on the children’s pauses. Results are generally consistent with the temporal behaviour matching hypothesis, i.e. children adapted the timing of their behaviour, e.g. by mirroring to the robot’s temporal behaviour.

This paper provides an accessible introduction to the cognitive systems paradigm of enaction and shows how it forms a practical framework for robotic systems that can develop cognitive abilities. The principal idea of enaction is that a cognitive system develops it own understanding of the world around it through its interactions with the environment.