Topology-Driven Anti-Entanglement Control for Soft Robots

TL;DR

This paper introduces a topology-driven multi-agent reinforcement learning framework to improve anti-entanglement control in soft robots operating in complex, constrained environments, enhancing coordination and stability.

Contribution

It proposes a novel topology-based reinforcement learning approach with centralized training and distributed execution to better prevent robot entanglement.

Findings

Outperforms existing DRL methods in convergence speed.

Demonstrates improved anti-winding effectiveness in simulations.

Enhances system reliability by eliminating inter-robot communication requirements.

Abstract

In the field of precision manufacturing in complex constrained environments, the role of soft robots is increasingly prominent, and the realization of anti-winding control based on multi-intelligent body reinforcement learning has become a research hotspot. One of the core problems at present is to coordinate multiple robots to complete the unwinding operation in a highly constrained environment. The existing distributed training framework faces some observability challenges in high-density barrier and unstable environments, resulting in poor learning results. This paper proposes a topology-driven Multi-Agent Reinforcement Learning (TD-MARL) framework to coordinate multi-robot systems to avoid entanglement. Specifically, the critical network adopts centralized learning, so that each intelligent body can perceive the strategies of other intelligent bodies by sharing the topological state, thus alleviating the training instability caused by complex interactions; eliminating the demand for communication resources between robots through distributed execution, Upgrade system reliability; the integrated topological security layer uses topological invariants to accurately assess and mitigate the risk of entanglement to avoid the strategy from falling into local difficulties. Finally, the full simulation experiments carried out in the real simulation environment show that the method is better than the current advanced deep reinforcement learning (DRL) method in terms of convergence and anti-winding effect.