A Neural Network-Based Approach to Robot Motion Control

Authors:
Uli Grasemann;Daniel Stronger;Peter Stone
Affiliations:
Department of Computer Sciences, University of Texas at Austin, Austin, USA TX 78712;Department of Computer Sciences, University of Texas at Austin, Austin, USA TX 78712;Department of Computer Sciences, University of Texas at Austin, Austin, USA TX 78712
Venue:
RoboCup 2007: Robot Soccer World Cup XI
Year:
2008

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Abstract

The joint controllers used in robots like the Sony Aibo are designed for the task of moving the joints of the robot to a given position. However, they are not well suited to the problem of making a robot move through a desired trajectory at speeds close to the physical capabilities of the robot, and in many cases, they cannot be bypassed easily. In this paper, we propose an approach that models both the robot's joints and its built-in controllers as a single system that is in turn controlled by a neural network. The neural network controls the entire trajectory of a robot instead of just its static position. We implement and evaluate our approach on a Sony Aibo ERS-7.