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.

翻译：协同搬运，即多个机器人协作搬运负载，近年来受到广泛关注。在狭窄环境中，可行区域极为有限，确保安全且高性能的机器人间协作对于有效执行任务至关重要，但实现起来十分困难。为应对这一挑战，我们提出了一种基于安全强化学习（RL）的双四足机器人协同搬运新方法。具体而言，我们将该任务建模为一个完全合作的约束马尔可夫博弈，其中避碰被表述为约束条件。我们引入了一种成本优势分解方法，该方法强制团队约束总和保持在某个上限以下，从而在RL框架内保证任务安全性。此外，我们提出了一种约束分配方法，将共享约束分配给个体机器人，以最大化整体任务奖励，鼓励机器人间自主进行任务分配，从而提升协作任务性能。仿真与实时实验结果表明，与现有方法相比，所提方法在双四足机器人协同搬运中实现了更优的性能和更高的成功率。