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

• URL: http://arxiv.org/abs/2302.13423v2
• Date: Sun, 17 Sep 2023 12:11:02 GMT
• Title: Sim-and-Real Reinforcement Learning for Manipulation: A Consensus-based Approach
• Authors: Wenxing Liu, Hanlin Niu, Wei Pan, Guido Herrmann, Joaquin Carrasco
• Abstract summary: We propose a Consensus-based Sim-And-Real deep reinforcement learning algorithm (CSAR) for manipulator pick-and-place tasks. We train the agents in simulators and the real world to get the optimal policies for both sim-and-real worlds.
• Score: 4.684126055213616
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 convergence 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.

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