NDLI: Learning compliance control laws for robotic assembly: an application of adaptive reinforcement learning control

Content Provider	IEEE Xplore Digital Library
Author	Boo-Ho Yang Asada, H.
Copyright Year	1993
Description	Author affiliation: Center for Inf. Driven Mech. Syst., MIT, Cambridge, MA, USA (Boo-Ho Yang; Asada, H.)
Abstract	The primary objective of this paper is to demonstrate that the adaptive reinforcement learning method proposed by Asada-Izumi (1989) is efficient and useful in learning a compliance control law in a class of robotic assembly tasks. A simple ball-aligning task is used as an example, where a robot is required to move a ball to the corner of a box. In the simulation, the robot is initially provided with only position feedback gains to follow the nominal trajectory. However, at each attempt, the location of the box is randomly deviated from the nominal position as uncertainty of the environment and, therefore, compliant motion control is required to guide the ball to the corner of the box. After repeating the collision with the walls of the box, the robot can successfully generate force feedback gains to modify its nominal motion. Our results show that the new learning method can be used to learn a compliance control law effectively.
Starting Page	1821
Ending Page	1824
File Size	395441
Page Count	4
File Format	PDF
ISBN	0780314212
DOI	10.1109/IJCNN.1993.717008
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1993-10-25
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robot control Robotic assembly Programmable control Adaptive control Force feedback Force control Force measurement Learning Education Motion control
Content Type	Text
Resource Type	Article

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