Presentation 2001/3/16
Acquisition of Reaching Movement Based on Reinforcement Learning
Katsunari SHIBATA, Masanori SUGISAKA, Koji ITO,
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Abstract(in English) A learning system with a neural network whose inputs are visual sensory signals and state of a manipulator, and whose outputs are joint torques, obtains the hand reaching movement by reinforcement learning. This paper shows some simulation results in the case of (1)variable link length and in the case of (2)external force which is loaded at the hand. In the former, a hidden neuron's representation is similar to the postcentral neurons of a monkey in Iriki's experiment. The neuron activates when the food is located at the place where the hand can reach it, and the range is extended to the range where a tool can reach when the monkey uses the tool. In the latter, it is shown that the system obtains inverse dynamics of its hand and environment. When it learns in the random force at shown trial, the system becomes to use feedback control mainly to control its hand.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Reinfbrcement Learning / Neural Network / Reaching Task / Tool / Inverse Dynamics / Feedback Control
Paper # NC2000-170
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Committee NC
Conference Date 2001/3/16(1days)
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Paper Information
Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Acquisition of Reaching Movement Based on Reinforcement Learning
Sub Title (in English)
Keyword(1) Reinfbrcement Learning
Keyword(2) Neural Network
Keyword(3) Reaching Task
Keyword(4) Tool
Keyword(5) Inverse Dynamics
Keyword(6) Feedback Control
1st Author's Name Katsunari SHIBATA
1st Author's Affiliation Faculty of Engineering, Oita University()
2nd Author's Name Masanori SUGISAKA
2nd Author's Affiliation Faculty of Engineering, Oita University
3rd Author's Name Koji ITO
3rd Author's Affiliation Dept. of Computational Intolligence & Systems Science, Tokyo Institute of Techinology
Date 2001/3/16
Paper # NC2000-170
Volume (vol) vol.100
Number (no) 688
Page pp.pp.-
#Pages 8
Date of Issue