Reinforcement Learning for Object Manipulation Using Low-dimensional Mapping

Abstract

This paper proposes a reinforcement learning method for dynamic control problems with holonomic constraints. The learning method is applicable to problems where the actual motion of the system is restricted to lower-dimensional submanifolds, so long as certain conditions are satisfied. Such dynamic control problems occur in robotic manipulation, which usually includes some holonomic constraints between the object and the robot or the environment. By introducing nonlinear mapping to one-dimensional space and approximating the boundary of a discontinuous reward function, the proposed method results in effective learning. The method is evaluated in a one degree of freedom object rotating task with contact force considerations. The effectiveness of the proposed learning method was verified by comparison to ordinal Q-learning and Dyna without the proposed mapping method.