Learning to Self-Reconfigure for Freeform Modular Robots via Altruism Multi-Agent Reinforcement Learning

Lei Wu, Bin Guo, Qiuyun Zhang, Zhuo Sun, Jieyi Zhang, Zhiwen Yu

Research output: Contribution to journalConference articlepeer-review

Abstract

Modular robots can change between different configurations to adapt to complex and dynamic environments. Therefore, performing accurate and efficient changes to modular robot system, known as the self-reconfiguration problem, is essential. Existing reconfiguration algorithms are based on discrete motion primitives. However, freeform modular robots are connected without alignment and their motion space is continuous, making existing reconfiguration methods infeasible. In this work, we design a parallel distributed self-reconfiguration algorithm based on multi-agent reinforcement learning for freeform modular robots. We introduce a collaboration mechanism into the reinforcement learning to avoid conflicts in continuous action spaces. Simulations show that our algorithm reduces conflicts and improves effectiveness compared to the baselines.

Original languageEnglish
Pages (from-to)2544-2546
Number of pages3
JournalProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume2023-May
StatePublished - 2023
Event22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023 - London, United Kingdom
Duration: 29 May 20232 Jun 2023

Keywords

  • Altruism Scale
  • Modular Robots
  • Reinforcement Learning
  • Self-reconfiguration

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