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arXiv · 2602.10514

Co-jump: Cooperative Jumping with Quadrupedal Robots via Multi-Agent Reinforcement Learning

Shihao Dong·Yeke Chen·Zeren Luo·Jiahui Zhang·Bowen Xu·Jinghan Lin·Yimin Han·Ji Ma·Zhiyou Yu·Yudong Zhao·Peng Lu
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Abstract

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.

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Shihao Dong, Yeke Chen, Zeren Luo, Jiahui Zhang, Bowen Xu, Jinghan Lin, Yimin Han, Ji Ma, Zhiyou Yu, Yudong Zhao, Peng Lu. 2026-02-11. Co-jump: Cooperative Jumping with Quadrupedal Robots via Multi-Agent Reinforcement Learning. https://arxiv.org/abs/2602.10514

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