標題: Reinforcement learning and robust control for robot compliance tasks
作者: Kuan, CP
Young, KY
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: compliance tasks;reinforcement learning;robust control
公開日期: 1-Oct-1998
摘要: The complexity in planning and control of robot compliance tasks mainly results from simultaneous control of both position and force and inevitable contact with environments. It is quite difficult to achieve accurate modeling of the interaction between the robot and the environment during contact. In addition, the interaction with the environment varies even for compliance tasks of the same kind. To deal with these phenomena, in this paper, we propose a reinforcement learning and robust control scheme for robot compliance tasks. A reinforcement learning mechanism is used to tackle variations among compliance tasks of the same kind. A robust compliance controller that guarantees system stability in the presence of modeling uncertainties and external disturbances is used to execute control commands sent from the reinforcement learning mechanism. Simulations based on deburring compliance tasks demonstrate the effectiveness of the proposed scheme.
URI: http://hdl.handle.net/11536/31846
ISSN: 0921-0296
期刊: JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
Volume: 23
Issue: 2-4
起始頁: 165
結束頁: 182
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