提出多模态扩散策略，让四足机器人自主完成复杂地形跑酷导航。
MulDP: Multimodal Diffusion Policy for Autonomous Quadruped Parkour Navigation across Complex Terrains
- 融合视觉、本体感知与目标信息生成连贯的运动指令。
- 在仿真和真实环境中实现长时程自主穿越复杂地形。
- 构建首个四足跑酷导航数据集，支持模型训练与评估。
四足机器人已在复杂地形的跑酷运动中展现出卓越灵活性，但多数系统仍依赖人工进行高层规划，自主跑酷导航仍处于探索阶段。核心挑战包括精细的速度调节、长时程的前瞻行为以及感知与具身执行间的紧密耦合。为此，我们提出多模态扩散策略（MulDP），将视觉感知、机器人本体感知与目标信息融合，生成时间连贯且具有前瞻性的导航速度指令，实现感知与具身控制的紧密耦合，从而实现鲁棒的自主导航。为支持MulDP训练，我们构建了首个四足跑酷导航数据集（QPND），涵盖多样化导航行为与复杂地形。大量仿真与真实世界实验表明，MulDP可实现鲁棒的长时程自主导航，并有效穿越复杂地形。
原文摘要 · Abstract (English)
Quadruped robots have demonstrated impressive agility in parkour locomotion across complex terrains. However, most systems still rely on human intervention for high-level planning, and autonomous parkour navigation remains underexplored. The key challenges include fine-grained velocity regulation, long-horizon anticipatory behaviors, and tight coupling between perception and embodied execution. To address these challenges, we propose a Multimodal Diffusion Policy (MulDP) that integrates visual perception with robot proprioception and goal information to generate temporally coherent and anticipatory navigation velocity commands, tightly coupling perception with embodied control to enable robust autonomous navigation. To support the training of MulDP, we construct the first Quadruped Parkour Navigation Dataset (QPND), a multimodal dataset that encompasses diverse navigation behaviors and complex terrains. Extensive simulation and real-world experiments demonstrate that MulDP enables robust long-horizon autonomous navigation and effective traversal across complex terrains.
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