Goal-directed imitation for robots: A bio-inspired approach to action understanding and skill learning |
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| Affiliation: | 1. Department of Mathematics for Science and Technology, University of Minho, 4800-058 Guimaraes, Portugal;2. Department of Industrial Electronics, University of Minho, 4800-058 Guimaraes, Portugal;3. Informatics, Chair for Robotics and Embedded Systems, Technical University Munich, 85748 Garching, Germany;4. Nijmegen Institute for Cognition and Information, Radboud University Nijmegen, 6500 HE Nijmegen, The Netherlands;1. School of Aerospace and Mechanical Engineering, Korea Aerospace University, Goyang, Gyeonggi-do, 412-791, South Korea;2. Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia;3. Department of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy;1. Grid and Cloud Computing Research Laboratory, Department of Computer Science, State University of New York (SUNY) at Binghamton, Vestal, NY 13902, United States;2. Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, United States |
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| Abstract: | In this paper we present a robot control architecture for learning by imitation which takes inspiration from recent discoveries in action observation/execution experiments with humans and other primates. The architecture implements two basic processing principles: (1) imitation is primarily directed toward reproducing the outcome of an observed action sequence rather than reproducing the exact action means, and (2) the required capacity to understand the motor intention of another agent is based on motor simulation. The control architecture is validated in a robot system imitating in a goal-directed manner a grasping and placing sequence displayed by a human model. During imitation, skill transfer occurs by learning and representing appropriate goal-directed sequences of motor primitives. The robustness of the goal-directed organization of the controller is tested in the presence of incomplete visual information and changes in environmental constraints. |
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