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

Imitation can be viewed as a means of enhancing learning in multiagent environments. It augments

Imitation is actively being studied as an effective means of learning in multi-agent environments. It allows an agent to learn how to act well (perhaps optimally) by passively observing the actions of cooperative teachers or other more experienced agents its environment. We propose a straightforward imitation mechanism called model extraction that can be integrated easily into standard model-based reinforcement learning algorithms. Roughly, by observing a mentor with similar capabilities, an agent can extract information about its own capabilities in unvisited parts of state space. The extracted information can accelerate learning dramatically. We illustrate the benefits of model extraction by integrating it with prioritized sweeping, and demonstrating improved performance and convergence through observation of single and multiple mentors. Though we make some stringent assumptions regarding observability, possible interactions and common abilities, we briefly comment on extensions of the model that relax these.

... this paper demonstrates scaling up of this movement imitative strategy for transmitting a vocabulary across a group of robotic agents, i.e. from a teacher agent to several learner agents. In particular, it shows that imitative behaviour is necessary for the grounding of the agents' proprioceptions and speeds up the grounding of exteroceptions. These studies stress the importance of behavioural social mechanisms in addition to general cognitive abilities of associativity for grounding communication in embodied agents. In particular, it shows that a simple movement imitation strategy is an interesting scenario for the transmission of a language, as it is an easy means of getting the agents to share a common context of perceptions, which is a prerequisite for a common understanding of the language to develop. It is thus suggested that a behaviour -oriented approach might be more appropriate than a pure cognitivist one which is dominating in related studies of the mechanisms involved in grounding communication.

Learning through imitation is a powerful and versatile method for acquiring new behaviors. In humans, a wide range of behaviors, from styles of social interaction to tool use, are passed from one generation to another through imitative learning. Although imitation evolved through Darwinian means, it achieves Lamarckian ends: it is a mechanism for the inheritance of acquired characteristics. Unlike trial-and-error-based learning methods such as reinforcement learning, imitation allows rapid learning.

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.

We study the problem of accelerating reinforcement learning (RL) through the observation and implicit imitation of expert agents (mentors) acting in the same domain. In this paper, we consider problems that arise when the learner and mentor have heterogeneous actions. We extend an earlier implicit imitation model to allow for feasibility testing (determining whether a specific mentor action can be duplicated) and repair (discovering a "plan" that simulates a mentor's trajectory) and demonstrate empirically that both of these components allow agents to learn much more readily than standard RL agents and implicit imitation agents without these capabilities. 1. Introduction Cooperative multiagent systems rely on shared models and communication to coordinate their actions in a common environment. While many researchers have examined explicit communication, we have argued (as have others) that implicit communication techniques such as imitation increase the range of applicati...

In multiagent environments, forms of social learning such as teaching and imitation have been shown to aid the transfer of knowledge from experts to learners in reinforcement learning (RL). We recast the problem of imitation in a Bayesian framework.

In this paper we discuss how agents can learn to do things b imitating other agents. Especially we look at how the use o different metrics and sub-goal granularity can affect the imitation results. We use a computer model of a chess world as a test-bed to also illustrate issues that arise when there is dissimilar embodiment between the demonstrator and the imitator agents.

We discuss the importance of narrative intelligence (story-awareness, story-telling, historical grounding) in regard to an agent's transcendence of its immediate local temporal context to create a broad temporal horizon in which the experience and future of the agent can be accounted for, together with the advantage that narrative provides to sociality by making the experience of others available without the risk of having to undergo the experience for one's self. Concepts and consequences for the design of artifacts are surveyed, together with a brief description of a formal algebraic framework a#ording support for narrative grounding. What's a Story For? We address what it is about narrative that makes it worthwhile for natural and artificial agents. Interest in narrative in literature, cultural studies, psychology and the arts is of course much older than in Artificial Intelligence (AI) - in some cases ancient - and has necessarily focused primarily on human notions of ...