Using Implicit Behavior Cloning and Dynamic Movement Primitive to
  Facilitate Reinforcement Learning for Robot Motion Planning
Zengjie Zhang Jayden Hong Amir Soufi Enayati Homayoun Najjaran
Reinforcement learning (RL) for motion planning of multi-degree-of-freedom
robots still suffers from low efficiency in terms of slow training speed and
poor generalizability. In this paper, we propose a novel RL-based robot motion
planning framework that uses implicit behavior cloning (IBC) and dynamic
movement primitive (DMP) to improve the training speed and generalizability of
an off-policy RL agent. IBC utilizes human demonstration data to leverage the
training speed of RL, and DMP serves as a heuristic model that transfers motion
planning into a simpler planning space. To support this, we also create a human
demonstration dataset using a pick-and-place experiment that can be used for
similar studies. Comparison studies in simulation reveal the advantage of the
proposed method over the conventional RL agents with faster training speed and
higher scores. A real-robot experiment indicates the applicability of the
proposed method to a simple assembly task. Our work provides a novel
perspective on using motion primitives and human demonstration to leverage the
performance of RL for robot applications.