Reinforcement Learning of Action Strategies and Joint Stiffness of Tendon-driven Biped Robot
| Project/Area Number |
21560275
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent mechanics/Mechanical systems
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| Research Institution | Meiji University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
TANAKA Sumio 明治大学, 理工学部, 講師 (40287884)
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| Co-Investigator(Renkei-kenkyūsha) |
HYODO Kazuhito 神奈川工科大学, 工学部, 教授 (10271371)
MIYAZAKI Kazuteru 独立行政法人大学評価・学位授与機構, 准教授 (20282866)
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| Project Period (FY) |
2009 – 2011
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| Project Status |
Completed (Fiscal Year 2011)
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| Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2011: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2010: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
Fiscal Year 2009: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
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| Keywords | 機械知能 / 知能ロボット / 制御工学 / 機械学習 / ロボティックス / 知能ロボティックス / 知能ロボティクス |
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| Research Abstract |
In this research, a learning method for robots to learn appropriate actions by profits and penalties given from the environment was developed and applied to action learning in the robotic succor game and walking movement of a biped robot. To apply it to the real robots and to improve the efficiency, a method to decide the criterion for penalties was considered and states in that the robot already had learned sufficiently were treated as a fixed-mode state(deterministic action strategy is used). Furthermore, the mechanism and control of a biped robot driven with motors(muscles) and wires(tendons) were considered. The tensile force control of tendons was done with robust stiffness-adjustable device, since tensile force up to 400N(40kgf) is expected during walking.
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Report
(4 results)
Research Products
(21 results)
