两只四足机器人协作跳跃，高度提升144%。
Co-jump: Cooperative Jumping with Quadrupedal Robots via Multi-Agent Reinforcement Learning
- 用多智能体强化学习让两机器人无通信协同跳跃。
- 实测跳到1.5米高平台，单机跳高提升至1.1米。
- 仅靠自身感知实现精准配合，适合受限环境协作。
尽管单智能体腿式运动已取得显著进展，但个体机器人仍受物理驱动能力限制。为突破这些局限，我们提出Co-jump任务：两只四足机器人同步协作，完成远超各自独立能力的跳跃。在去中心化设置下，针对高冲击接触动力学挑战，我们采用增强型多智能体近端策略优化（MAPPO）与渐进式课程策略，有效克服机械耦合系统中稀疏奖励带来的探索难题。实验在仿真中验证了鲁棒性能，并成功迁移到真实硬件，实现多方向跳上最高达1.5米的平台。其中，单机器人脚端抬升高度达1.1米，相比独立四足机器人0.45米的跳跃高度提升144%，展现出优异垂直性能。值得注意的是，这种精确协调仅通过本体感知反馈实现，为受限环境中无需通信的协作运动奠定了基础。
原文摘要 · Abstract (English)
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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