Sim-and-Real Reinforcement Learning for Manipulation: A Consensus-based Approach

Wenxing Liu; Hanlin Niu; Wei Pan; Guido Herrmann; Joaquin Carrasco

ICRA 2023poster9 citations

Sim-and-Real Reinforcement Learning for Manipulation: A Consensus-based Approach

Wenxing Liu, Hanlin Niu, Wei Pan, Guido Herrmann, Joaquin Carrasco

Abstract

Sim-and-real training is a promising alternative to sim-to-real training for robot manipulations. However, the current sim-and-real training is neither efficient, i.e., slow con-vergence to the optimal policy, nor effective, i.e., sizeable real-world robot data. Given limited time and hardware budgets, the performance of sim-and-real training is not satisfactory. In this paper, we propose a Consensus-based Sim-And-Real deep reinforcement learning algorithm (CSAR) for manipulator pick-and-place tasks, which shows comparable performance in both sim-and- real worlds. In this algorithm, we train the agents in simulators and the real world to get the optimal policies for both sim-and-real worlds. We found two interesting phenomenons: (1) Best policy in simulation is not the best for sim-and-real training. (2) The more simulation agents, the better sim-and-real training. The experimental video is available at: https://youtu.be/mcHJtNIsTEQ.

BibTeX

@inproceedings{icra2023_simandrealreinfo,
  title = {Sim-and-Real Reinforcement Learning for Manipulation: A Consensus-based Approach},
  author = {Wenxing Liu and Hanlin Niu and Wei Pan and Guido Herrmann and Joaquin Carrasco},
  booktitle = {ICRA 2023},
  year = {2023}
}