We want to build robots capable of rich social interactions with humans, including natural communication and cooperation. This work explores how imitation as a social learning and teaching process may be applied to building socially intelligent robots, and summarizes our progress toward building a robot capable of learning how to imitate facial expressions from simple imitative games played with a human, using biologically inspired mechanisms. Our approach is heavily influenced by the ways human infants learn to communicate with their caregivers and understand the actions of others in intentional terms. Among the key ideas that we draw from work on the development of human social intelligence, the most crucial is the hypothesis that in human infants, imitative interactions, starting with facial mimicry, are a significant stepping-stone in developing appropriate social behavior, learning to predict other’s actions, and ultimately, understanding the intensions of others. 1

Imitation is a powerful mechanism whereby knowledge may be transferred between agents (both biological and artificial). Key problems on the topic of imitation have emerged in various areas close to artificial intelligence, including the cognitive and social sciences, animal behavior, robotics, human--computer interaction, embodied intelligence, software engineering, programming by example and machine learning. Artificial systems used to study imitation can both test models of imitation derived from observational or neurobiological data on imitation in animals and then apply them to different kinds of nonbiological systems ranging from robots to software agents. A crucial problem in imitation is the correspondence problem, mapping action sequences of the demonstrator and the imitator agent. This problem becomes particularly obvious when the two agents do not share the same embodiment and affordances. This paper describes a new general imitation mechanism called Action Learning for Imitation via Correspondence between embodiments (ALICE) that specifically addresses the correspondence problem. The mechanism is implemented and its efficacy illustrated on the "chessworld" testbed that was created to study imitation from an agent-based perspective, i.e., by a particular agent in a particular environment.

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.

Affordances encode relationships between actions, objects, and effects. They play an important role on basic cognitive capabilities such as prediction and planning. We address the problem of learning affordances through the interaction of a robot with the environment, a key step to understand the world properties and develop social skills. We present a general model for learning object affordances using Bayesian networks integrated within a general developmental architecture for social robots. Since learning is based on a probabilistic model, the approach is able to deal with uncertainty, redundancy, and irrelevant information. We demonstrate successful learning in the real world by having an humanoid robot interacting with objects. We illustrate the benefits of the acquired knowledge in imitation games.

Abstract: It is widely believed that what distinguishes the social cognition of humans from that of other animals is the belief-desire psychology of four-year-old children and adults (so-called theory of mind). We argue here that this is actually the second ontogenetic step in uniquely human social cognition. The first step is one year old children’s understanding of persons as intentional agents, which enables skills of cultural learning and shared intentionality. This initial step is ‘the real thing ’ in the sense that it enables young children to participate in cultural activities using shared, perspectival symbols with a conventional/normative/reflective dimension—for example, linguistic communication and pretend play—thus inaugurating children’s understanding of things mental. Understanding beliefs and participating in collective intentionality at four years of age—enabling the comprehension of such things as money and marriage—results from several years of engagement with other persons in perspective-shifting and reflective discourse containing propositional attitude constructions. By all appearances, the cognitive skills of human beings are very different from those of other animal species, including our nearest primate relatives. Human

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this article, for lack of a better term, we shall use the word "imitator" to refer to any autonomous agent performing a candidate behavioral match. The use of this word here does not entail any particular mechanism of matching or any particular type of social learning. In what follows, we shall describe how different matching phenomena arise depending on the criteria employed in generating the behavior of the imitator. For example, goal emulation, stimulus enhancement, mimicry, and so on, will all be cast as solutions to correspondence problems with different particular selection criteria

In this paper we propose a computational model for learning from demonstration. By adequate adjustment of a few parameters, our model is able to produce di erent learning behaviours, taking into account di erent elements of the demonstration. In particular, our model takes into consideration the actions of the demonstrator, its effects on the environment/surroundings, the demonstrator's inferred goals, and the interests and preferences of the learner itself. We present results where we show that our model can reproduce (in simulation) several well-known results from standard experimental paradigms in developmental psychology and also an application to a real robotic imitation learning task.

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