Learning reactive admittance control

A peg-in-hole insertion task is used as an example to illustrate the utility of direct associative reinforcement learning methods for learning control under real-world conditions of uncertainty and noise. An associative reinforcement learning system has to learn appropriate actions in various situations through a search guided by evaluative performance feedback The authors used such a learning system, implemented as a connectionist network, to learn active compliant control for peg-in-hole insertion. The results indicated that direct reinforcement learning can be used to learn a reactive control strategy that works well even in the presence of a high degree of noise and uncertainty