Low-Rank Modular Reinforcement Learning via Muscle Synergy

• URL: http://arxiv.org/abs/2210.15479v1
• Date: Wed, 26 Oct 2022 16:01:31 GMT
• Title: Low-Rank Modular Reinforcement Learning via Muscle Synergy
• Authors: Heng Dong, Tonghan Wang, Jiayuan Liu, Chongjie Zhang
• Abstract summary: Modular Reinforcement Learning (RL) decentralizes the control of multi-joint robots by learning policies for each actuator. We propose a Synergy-Oriented LeARning (SOLAR) framework that exploits the redundant nature of DoF in robot control.
• Score: 25.120547719120765
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Modular Reinforcement Learning (RL) decentralizes the control of multi-joint robots by learning policies for each actuator. Previous work on modular RL has proven its ability to control morphologically different agents with a shared actuator policy. However, with the increase in the Degree of Freedom (DoF) of robots, training a morphology-generalizable modular controller becomes exponentially difficult. Motivated by the way the human central nervous system controls numerous muscles, we propose a Synergy-Oriented LeARning (SOLAR) framework that exploits the redundant nature of DoF in robot control. Actuators are grouped into synergies by an unsupervised learning method, and a synergy action is learned to control multiple actuators in synchrony. In this way, we achieve a low-rank control at the synergy level. We extensively evaluate our method on a variety of robot morphologies, and the results show its superior efficiency and generalizability, especially on robots with a large DoF like Humanoids++ and UNIMALs.

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