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.

翻译：尽管单智能体腿式运动已取得显著进展，但单个机器人仍从根本上受限于物理驱动能力。为突破这些限制，我们提出了协同跳跃任务，即两个四足机器人通过同步协作执行远超其单独能力的跳跃动作。我们在去中心化设定下处理该任务涉及的高冲量接触动力学，实现了无需显式通信或预定义运动基元的同步控制。本框架采用通过渐进课程策略增强的多智能体近端策略优化算法，有效克服了机械耦合系统中固有的稀疏奖励探索难题。我们在仿真中验证了其鲁棒性能，并成功迁移至实体硬件，实现了最高1.5米平台的多向跳跃。特别地，其中一个机器人实现了1.1米的足端抬升高度，相较于单四足机器人0.45米的跳跃高度提升了144%，展现出卓越的垂直运动性能。值得注意的是，这种精确协调仅通过本体感知反馈实现，为受限环境下无需通信的协作运动奠定了理论基础。