A Neural Network-Based Approach to Robot Motion Control (2008)
Uli Grasemann, Daniel Stronger, and Peter Stone
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.
In RoboCup-2007: Robot Soccer World Cup XI, Ubbo Visser and Fernando Ribeiro and Takeshi Ohashi and Frank Dellaert (Eds.), Vol. 5001, pp. 480-87, Berlin 2008. Springer Verlag.

Peter Stone Faculty pstone [at] cs utexas edu
Daniel Stronger Ph.D. Alumni dan stronger [at] gmail com