Octopus-inspired Distributed Control for Soft Robotic Arms: A Graph Neural Network-Based Attention Policy with Environmental Interaction

2026-03-10

AI Summary

This study addresses the challenge of enabling soft robotic arms to autonomously navigate and reach targets in unknown obstacle-rich environments using only local contact information. Inspired by octopus locomotion, the authors model a segmented soft arm as a multi-agent system and propose the first distributed control strategy that integrates graph attention mechanisms with online obstacle discovery. The approach constructs an interaction graph via graph neural networks and employs a centralized training–decentralized execution framework with a two-stage graph attention message-passing scheme for coordinated control. Evaluated on complex wall-penetrating tasks, the method significantly outperforms six state-of-the-art multi-agent reinforcement learning baselines, demonstrating high success rates and low energy consumption even under observation noise, single-segment failures, and external disturbances, thereby validating its robustness and efficiency.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Multiagent Systems: Adversarial Agents
• Search and Optimization: Learning to Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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 relying on global obstacle geometry. 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 preserves success while keeping control effort bounded, indicating resilient coordination driven by selective contact-relevant information routing.

Problem

• soft robotic arms
• distributed control
• contact-rich environments
• obstacle avoidance
• robustness

Innovation

• graph neural network
• distributed control
• soft robotics
• multi-agent reinforcement learning
• attention mechanism