基于多智能体强化学习（MARL）的四足机器人团队缆索牵引负载分布式导航方法
Decentralized Navigation of a Cable-Towed Load using Quadrupedal Robot Team via MARL
AI总结：
针对四足机器人团队缆索牵引负载时的混合物理交互与规模扩展难题，提出基于CTDE框架和定制训练课程的分布式MARL规划器，实现可变规模机器人的高效协同导航，兼具鲁棒性与适应性。
摘要：
本研究旨在解决四足机器人团队在杂乱非结构化环境中协同牵引缆索连接的负载、同时规避障碍物的难题。借助缆索，多机器人系统可在必要时保持松弛状态，从而通过狭窄空间。但这也带来了由张紧和松弛状态交替导致的混合物理交互问题，其计算复杂度会随智能体数量增加呈指数级增长。为应对这些挑战，我们开发了一套可扩展的分布式系统，能够动态协调数量可变的四足机器人，同时处理负载牵引任务中固有的混合物理交互。该系统的核心是一种新型的基于多智能体强化学习（MARL，首次出现补充中文：多智能体强化学习）的规划器，专为分布式协同设计。该基于MARL的规划器采用集中训练-分布式执行（CTDE，首次出现补充中文：集中训练与分布式执行）框架训练，使每个机器人仅依靠局部（自身）观测就能自主决策。为加快学习速度并确保团队规模变化时仍能有效协作，我们为MARL设计了定制化的训练课程。实验结果凸显了该框架的灵活性与可扩展性：在真实场景中成功部署了1至4台机器人，仿真环境中最多可支持12台机器人；无论团队规模如何，分布式规划器的推理时间始终保持一致。此外，该系统对环境扰动具有鲁棒性，且能适应不同的负载重量。本研究为在复杂真实环境中实现灵活高效的多足机器人协作迈出了重要一步。
英文摘要：
This work addresses the challenge of enabling a team of quadrupedal robots to collaboratively tow a cable-connected load through cluttered and unstructured environments while avoiding obstacles. Leveraging cables allows the multi-robot system to navigate narrow spaces by maintaining slack when necessary. However, this introduces hybrid physical interactions due to alternating taut and slack states, with computational complexity that scales exponentially as the number of agents increases. To tackle these challenges, we developed a scalable and decentralized system capable of dynamically coordinating a variable number of quadrupedal robots while managing the hybrid physical interactions inherent in the load-towing task. At the core of this system is a novel multi-agent reinforcement learning (MARL)-based planner, designed for decentralized coordination. The MARL-based planner is trained using a centralized training with decentralized execution (CTDE) framework, enabling each robot to make decisions autonomously using only local (ego) observations. To accelerate learning and ensure effective collaboration across varying team sizes, we introduce a tailored training curriculum for MARL. Experimental results highlight the flexibility and scalability of the framework, demonstrating successful deployment with one to four robots in real-world scenarios and up to twelve robots in simulation. The decentralized planner maintains consistent inference times, regardless of the team size. Additionally, the proposed system demonstrates robustness to environment perturbations and adaptability to varying load weights. This work represents a step forward in achieving flexible and efficient multi-legged robotic collaboration in complex and real-world environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
