Dual-Quadruped Collaborative Transportation in Narrow Environments via Safe Reinforcement Learning

TL;DR

This paper introduces a safe reinforcement learning approach for dual-quadruped robots to collaboratively transport payloads in narrow environments, ensuring safety and optimizing task performance.

Contribution

It presents a novel constrained Markov game model with cost-advantage decomposition and constraint allocation methods for safe, efficient multi-robot collaboration.

Findings

Achieves higher success rates in simulations and real experiments.

Ensures safety through constraint enforcement in reinforcement learning.

Improves collaborative task efficiency with autonomous constraint sharing.

Abstract

Collaborative transportation, where multiple robots collaboratively transport a payload, has garnered significant attention in recent years. While ensuring safe and high-performance inter-robot collaboration is critical for effective task execution, it is difficult to pursue in narrow environments where the feasible region is extremely limited. To address this challenge, we propose a novel approach for dual-quadruped collaborative transportation via safe reinforcement learning (RL). Specifically, we model the task as a fully cooperative constrained Markov game, where collision avoidance is formulated as constraints. We introduce a cost-advantage decomposition method that enforces the sum of team constraints to remain below an upper bound, thereby guaranteeing task safety within an RL framework. Furthermore, we propose a constraint allocation method that assigns shared constraints to individual robots to maximize the overall task reward, encouraging autonomous task-assignment among robots, thereby improving collaborative task performance. Simulation and real-time experimental results demonstrate that the proposed approach achieves superior performance and a higher success rate in dual-quadruped collaborative transportation compared to existing methods.