受章鱼启发的分布式控制让软体机械臂在未知环境中更灵活地抓取目标。
Octopus-inspired Distributed Control for Soft Robotic Arms: A Graph Neural Network-Based Attention Policy with Environmental Interaction
- 将每段机械臂视为智能体，用图注意力网络建模与环境的交互关系。
- 在有洞墙任务中优于6种主流多智能体强化学习方法，且控制能耗稳定。
- 适合需要抗干扰、自适应接触环境的柔性机器人应用。
本文提出SoftGM，一种受章鱼启发的分布式控制架构，用于分段式软体机械臂在接触密集型环境中的目标抓取。该方法不假设每轮起始时已知障碍物完整几何信息，而是通过在线发现障碍物。将机械臂各段视为协作智能体，以图结构表示臂-环境交互，采用中央训练、分散执行（CTDE）框架，结合中心化评价器与去中心化执行器，并设计双阶段图注意力消息传递机制。在基于Cosserat杆模型的PyElastica仿真器中，针对无障碍、结构化障碍和带孔墙壁三类任务进行评估。与六种主流多智能体强化学习基线（IDDPG、IPPO、ISAC、MADDPG、MAPPO、MASAC）在相同信息量和训练条件下对比，SoftGM在简单场景中达到强基线水平，在带孔墙任务中表现最优。鲁棒性测试显示，面对观测噪声、单段驱动故障及瞬时扰动，SoftGM仍保持性能稳定，控制能量可控，表明选择性接触相关信道路由提升了系统韧性。
原文摘要 · Abstract (English)
This paper proposes SoftGM, an octopus-inspired distributed control architecture for segmented soft robotic arms that learn to reach targets in contact-rich environments using online obstacle discovery without assuming full obstacle geometry at the start of each episode. SoftGM formulates each arm section as a cooperative agent and represents the arm-environment interaction as a graph. SoftGM uses a two-stage graph attention message passing scheme following a Centralised Training Decentralised Execution (CTDE) paradigm with a centralised critic and decentralised actor. We evaluate SoftGM in a Cosserat-rod simulator (PyElastica) across three tasks that increase the complexity of the environment: obstacle-free, structured obstacles, and a wall-with-hole scenario. Compared with six widely used MARL baselines (IDDPG, IPPO, ISAC, MADDPG, MAPPO, MASAC) under identical information content and training conditions, SoftGM matches strong CTDE methods in simpler settings and achieves the best performance in the wall-with-hole task. Robustness tests with observation noise, single-section actuation failure, and transient disturbances show that SoftGM maintains comparable performance under non-ideal simulated conditions while keeping control effort bounded, suggesting that selective contact-relevant information routing improves resilience in the tested settings.
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