Recently, quadrupedal locomotion has achieved significant success, but their manipulation capabilities, particularly in handling large objects, remain limited, restricting their usefulness in demanding real-world applications such as search and rescue, construction, industrial automation, and room organization. This paper tackles the task of obstacle-aware, long-horizon pushing by multiple quadrupedal robots. We propose a hierarchical multi-agent reinforcement learning framework with three levels of control. The high-level controller integrates an RRT planner and a centralized adaptive policy to generate subgoals, while the mid-level controller uses a decentralized goal-conditioned policy to guide the robots toward these sub-goals. A pre-trained low-level locomotion policy executes the movement commands. We evaluate our method against several baselines in simulation, demonstrating significant improvements over baseline approaches, with 36.0% higher success rates and 24.5% reduction in completion time than the best baseline. Our framework successfully enables long-horizon, obstacle-aware manipulation tasks like Push-Cuboid and Push-T on Go1 robots in the real world.

翻译：近年来，四足机器人的运动控制已取得显著进展，但其操控能力，尤其是在处理大型物体方面，仍然有限，这限制了它们在搜索救援、建筑施工、工业自动化及室内整理等要求严苛的实际应用中的效用。本文研究了多台四足机器人在障碍物环境下的长时程推动任务。我们提出了一种具有三层控制结构的层次化多智能体强化学习框架。高层控制器整合了RRT规划器与集中式自适应策略以生成子目标，中层控制器则采用去中心化的目标条件策略引导机器人朝向这些子目标运动。预训练的低层运动策略负责执行具体的移动指令。我们在仿真环境中将所提方法与多种基线方法进行比较评估，结果表明其性能显著优于基线方法，成功率比最佳基线提高了36.0%，完成时间缩短了24.5%。该框架成功地在现实世界的Go1机器人上实现了如立方体推动（Push-Cuboid）和T型物体推动（Push-T）等长时程、障碍物感知的操控任务。