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This paper presents a modular neural controller that learns goal-oriented obstacle-avoiding motion strategies for a sensor-based three-link planar robot arm. It acquires these strategies through reinforcement learning from local sensory data. The controller has two reinforcement-based modules: a module for negotiating obstacles and a module for moving to the goal. Both modules generate actions that are interpreted with regard to a goal vector in the robot joint space. A differential inverse kinematics (DIV) module is used to obtain such a goal vector. The DIV module is based on the inversion of a neural network that has been previously trained to approximate the manipulator forward kinematics in polar coordinates. The controller achieves a satisfactory performance quite rapidly and shows good generalization capabilities in the face of new environments.

Additional information

Authors: MARTÍN P, Department of Computer Science, University of Jaume I, Castellón (ES);MILLÁN J, JRC Ispra (IT);MARTIN P, Department of Computer Science, University of Jaume I, Castellon (ES);MILLAN J, JRC Ispra (IT)
Bibliographic Reference: Paper presented: IEEE/RSJ International Conference of Intelligent Robots and Systems, Grenoble (FR), September 8-13, 1997
Availability: Available from (1) as Paper EN 40703 ORA
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