Learning Multi-Agent Loco-Manipulation for Long-Horizon Quadrupedal Pushing
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916665455804416 |
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| author | Feng, Yuming Hong, Chuye Niu, Yaru Liu, Shiqi Yang, Yuxiang Yu, Wenhao Zhang, Tingnan Tan, Jie Zhao, Ding |
| author_facet | Feng, Yuming Hong, Chuye Niu, Yaru Liu, Shiqi Yang, Yuxiang Yu, Wenhao Zhang, Tingnan Tan, Jie Zhao, Ding |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_07104 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Multi-Agent Loco-Manipulation for Long-Horizon Quadrupedal Pushing Feng, Yuming Hong, Chuye Niu, Yaru Liu, Shiqi Yang, Yuxiang Yu, Wenhao Zhang, Tingnan Tan, Jie Zhao, Ding Robotics Artificial Intelligence Machine Learning Multiagent Systems 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. |
| title | Learning Multi-Agent Loco-Manipulation for Long-Horizon Quadrupedal Pushing |
| topic | Robotics Artificial Intelligence Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2411.07104 |