CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
The Induction of Dynamical Recognizers
Machine Learning - Connectionist approaches to language learning
Modeling parietal-premotor interactions in primate control of grasping
Neural Networks - Special issue on neural control and robotics: biology and technology
Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
Neural Networks - Special issue on organisation of computation in brain-like systems
Imitation in animals and artifacts
Imitation in animals and artifacts
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Journal of Cognitive Neuroscience
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Model-based learning for mobile robot navigation from the dynamicalsystems perspective
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On Learning, Representing, and Generalizing a Task in a Humanoid Robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper introduces a novel neuro-dynamical model that accounts for possible mechanisms of action imitation and learning. It is considered that imitation learning requires at least two classes of generalization. One is generalization over sensory-motor trajectory variances, and the other class is on cognitive level which concerns on more qualitative understanding of compositional actions by own and others which do not necessarily depend on exact trajectories. This paper describes a possible model dealing with these classes of generalization by focusing on the problem of action compositionality. The model was evaluated in the experiments using a small humanoid robot. The robot was trained with a set of different actions concerning object manipulations which can be decomposed into sequences of action primitives. Then the robot was asked to imitate a novel compositional action demonstrated by a human subject which are composed from prior-learned action primitives. The results showed that the novel action can be successfully imitated by decomposing and composing it with the primitives by means of organizing unified intentional representation hosted by mirror neurons even though the trajectory-level appearance is different between the ones of observed and those of self-generated.