Combining reinforcement learning and differential inverse kinematics for collision-free motion of multilink manipulators
This paper presents a class of neural controllers that learn goal oriented obstacle-avoiding strategies for multilink manipulators. They acquire these strategies on-line through reinforcement learning from local sensory data. These controllers are mainly made of two neural modules: a reinforcement based action generator and a module for differential inverse kinematics (DIV). The action generator computes actions with regard to a goal vector in the manipulator joint space. Suitable goal vectors are provided by the DIV module. This module is based on the inversion of a neural network that has been previously trained to approximate the manipulator forward kinematics in polar coordinates. Results for 2-link and 3-link planar manipulators are shown. These controllers achieve a good performance quite rapidly and exhibit good generalization capabilities in the face of new environments.
Bibliographic Reference: Paper presented: International Work Conference on Artificial and Natural Neural Networks, Lanzarote (ES), June 4-6, 1997
Availability: Available from (1) as paper EN 40562 ORA
Record Number: 199711089 / Last updated on: 1997-09-16
Original language: en
Available languages: en