COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The agent-based perspective on imitation
Imitation in animals and artifacts
Learning Movement Sequences from Demonstration
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
Learning for control from multiple demonstrations
Proceedings of the 25th international conference on Machine learning
Combined structure and motion extraction from visual data using evolutionary active learning
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Nonlinear System Identification Using Coevolution of Models and Tests
IEEE Transactions on Evolutionary Computation
Correspondence Mapping Induced State and Action Metrics for Robotic Imitation
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A Developmental Roadmap for Learning by Imitation in Robots
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Infants exploit the perception that others are 'like me' to bootstrap social cognition (Meltzoff, 2007a). This paper demonstrates how the above theory can be instantiated in a social robot that uses itself as a model to recognize structural similarities with other robots; this thereby enables the student to distinguish between appropriate and inappropriate teachers. This is accomplished by the student robot first performing self-discovery, a phase in which it uses actuation-perception relationships to infer its own structure. Second, the student models a candidate teacher using a vision-based active learning approach to create an approximate physical simulation of the teacher. Third, the student determines that the teacher is structurally similar (but not necessarily visually similar) to itself if it can find a neural controller that allows its self model (created in the first phase) to reproduce the perceived motion of the teacher model (created in the second phase). Fourth, the student uses the neural controller (created in the third phase) to move, resulting in imitation of the teacher. Results with a physical student robot and two physical robot teachers demonstrate the effectiveness of this approach. The generalizability of the proposed model allows it to be used over variations in the demonstrator: The student robot would still be able to imitate teachers of different sizes and at different distances from itself, as well as different positions in its field of view, because change in the interrelations of the teacher's body parts are used for imitation, rather than absolute geometric properties.