Learning the peg-into-hole assembly operation with a connectionist reinforcement technique

摘要

The paper presents a learning controller that is capable of increasing insertion speed during consecutive peg-into-hole operations, without increasing the contact force level. Our aim is to find a better relationship between measured forces and the controlled velocity, without using a complicated (human generated) model. We followed a connectionist approach. Two learning phases are distinguished. First the learning controller is trained (or initialised) in a supervised way by a suboptimal task frame controller. Then a reinforcement learning phase follows. The controller consists of two networks: (1) the policy network and (2) the exploration network. On-line robotic exploration plays a crucial role in obtaining a better policy. Optionally, this architecture can be extended with a third network: the reinforcement network. The learning controller is implemented on a CADbased contact force simulator. In contrast with most other related work, the experiments are simulated in 3D with 6 degr...