ABSTRACT
SUMMARY This chapter presents how to implement machine learning in robotic applications. In particular, reinforcement learning or Q learning is introduced and demonstrated in physical robotic tasks. The first case study shows how to integrate reinforcement learning with genetic algorithms to provide decision making for a multi-robot transportation system. The second case study demonstrates how to employ reinforcement learning to improve the reliability of visual servoing controllers. The corresponding simulation and experimental results are presented.
