A modular reinforcement-based neural controller for a three-link manipulator

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 mod...

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Bibliographic Details
Main Authors: Martin, P., Millan, J.D.R.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Computer science
Learning
Manipulators
Motion control
Neural networks
Orbital robotics
Robot kinematics
Robot sensing systems
Robotics and automation
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Summary: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.
DOI:10.1109/IROS.1997.655100