Acquiring robot skills via reinforcement learning

Skill acquisition is a difficult , yet important problem in robot performance. The authors focus on two skills, namely robotic assembly and balancing and on two classic tasks to develop these skills via learning: the peg-in hole insertion task, and the ball balancing task. A stochastic real-valued (SRV) reinforcement learning algorithm is described and used for learning control and the authors show how it can be used with nonlinear multilayer ANNs. In the peg-in-hole insertion task the SRV network successfully learns to insert to insert a peg into a hole with extremely low clearance, in spite of high sensor noise. In the ball balancing task the SRV network successfully learns to balance the ball with minimal feedback.<>