ROBOT TRAJECTORY LEARNING FOR DYNAMIC TASKS

Abstract

Reinforcement learning can be used for robot trajectory planning. However, the existing methods have difficulties applying in physical robots and dynamic robot tasks. In this thesis, we optimize current approaches and propose new methods to apply reinforcement learning algorithms in robot trajectory planning problems. Our methods can plan trajectories for robot tasks with complex dynamics efficiently. The methods are effective in both simulations and real-world experiments.