JRM Vol.22 No.4 pp. 542-550
doi: 10.20965/jrm.2010.p0542


Learning of Whole Arm Manipulation with Constraint of Contact Mode Maintaining

Nobuyuki Kawarai and Yuichi Kobayashi

Tokyo University of Agriculture and Technology, 2-14-16 Naka-cho, Koganei, Tokyo 184-8588, Japan

December 21, 2009
April 21, 2010
August 20, 2010
reinforcement learning, manipulation, contact mode estimation
This paper proposes the learning of whole arm manipulation with a two-link manipulator. Our proposal combines a controller obtained by reinforcement learning (actor-critic) and a learning classifier realized by a Support Vector Machine (SVM). The classifier learns the boundary between slip and stick modes in torque space. Using the result of classification, the robot learns to move the object toward desired position while keeping the desired contact modes. Control input (torque) is first specified by the actor. The SVM classifier judges whether torque can maintain the desired slip or stick mode and, if not, it modifies the torque so that the desired mode is maintained. It was verified in the simulation that our proposed learning realized accelerating of the object and decelerating it while keeping the desired mode, i.e., avoiding undesired slipping of the object.
Cite this article as:
N. Kawarai and Y. Kobayashi, “Learning of Whole Arm Manipulation with Constraint of Contact Mode Maintaining,” J. Robot. Mechatron., Vol.22 No.4, pp. 542-550, 2010.
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