Co-jump: Cooperative Jumping with Quadrupedal Robots via Multi-Agent Reinforcement Learning
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arXiv
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| Format: | Preprint |
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2026
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| author | Dong, Shihao Chen, Yeke Luo, Zeren Zhang, Jiahui Xu, Bowen Lin, Jinghan Han, Yimin Ma, Ji Yu, Zhiyou Zhao, Yudong Lu, Peng |
| author_facet | Dong, Shihao Chen, Yeke Luo, Zeren Zhang, Jiahui Xu, Bowen Lin, Jinghan Han, Yimin Ma, Ji Yu, Zhiyou Zhao, Yudong Lu, Peng |
| contents | While single-agent legged locomotion has witnessed remarkable progress, individual robots remain fundamentally constrained by physical actuation limits. To transcend these boundaries, we introduce Co-jump, a cooperative task where two quadrupedal robots synchronize to execute jumps far beyond their solo capabilities. We tackle the high-impulse contact dynamics of this task under a decentralized setting, achieving synchronization without explicit communication or pre-specified motion primitives. Our framework leverages Multi-Agent Proximal Policy Optimization (MAPPO) enhanced by a progressive curriculum strategy, which effectively overcomes the sparse-reward exploration challenges inherent in mechanically coupled systems. We demonstrate robust performance in simulation and successful transfer to physical hardware, executing multi-directional jumps onto platforms up to 1.5 m in height. Specifically, one of the robots achieves a foot-end elevation of 1.1 m, which represents a 144% improvement over the 0.45 m jump height of a standalone quadrupedal robot, demonstrating superior vertical performance. Notably, this precise coordination is achieved solely through proprioceptive feedback, establishing a foundation for communication-free collaborative locomotion in constrained environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_10514 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Co-jump: Cooperative Jumping with Quadrupedal Robots via Multi-Agent Reinforcement Learning Dong, Shihao Chen, Yeke Luo, Zeren Zhang, Jiahui Xu, Bowen Lin, Jinghan Han, Yimin Ma, Ji Yu, Zhiyou Zhao, Yudong Lu, Peng Robotics Artificial Intelligence Machine Learning While single-agent legged locomotion has witnessed remarkable progress, individual robots remain fundamentally constrained by physical actuation limits. To transcend these boundaries, we introduce Co-jump, a cooperative task where two quadrupedal robots synchronize to execute jumps far beyond their solo capabilities. We tackle the high-impulse contact dynamics of this task under a decentralized setting, achieving synchronization without explicit communication or pre-specified motion primitives. Our framework leverages Multi-Agent Proximal Policy Optimization (MAPPO) enhanced by a progressive curriculum strategy, which effectively overcomes the sparse-reward exploration challenges inherent in mechanically coupled systems. We demonstrate robust performance in simulation and successful transfer to physical hardware, executing multi-directional jumps onto platforms up to 1.5 m in height. Specifically, one of the robots achieves a foot-end elevation of 1.1 m, which represents a 144% improvement over the 0.45 m jump height of a standalone quadrupedal robot, demonstrating superior vertical performance. Notably, this precise coordination is achieved solely through proprioceptive feedback, establishing a foundation for communication-free collaborative locomotion in constrained environments. |
| title | Co-jump: Cooperative Jumping with Quadrupedal Robots via Multi-Agent Reinforcement Learning |
| topic | Robotics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2602.10514 |