MulDP：用于四足机器人穿越复杂地形自主跑酷导航的多模态扩散策略
MulDP: Multimodal Diffusion Policy for Autonomous Quadruped Parkour Navigation across Complex Terrains
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中文总结 AI 辅助
本文针对四足机器人自主跑酷导航的关键挑战，提出MulDP多模态扩散策略，构建QPND数据集，经仿真与真实实验验证其可实现复杂地形的长时程鲁棒自主导航。
中文摘要 AI 辅助
四足机器人在复杂地形的跑酷运动中已展现出出色的敏捷性，但大多数系统仍依赖人类干预进行高层规划，自主跑酷导航的研究尚不充分。关键挑战包括精细的速度调节、长时程的前瞻性行为以及感知与具身执行的紧密耦合。为应对这些挑战，本文提出一种多模态扩散策略（Multimodal Diffusion Policy，MulDP），该策略将视觉感知与机器人本体感知、目标信息相融合，生成时间连贯且具前瞻性的导航速度指令，实现感知与具身控制的紧密耦合，以支持鲁棒的自主导航。为支撑MulDP的训练，本文构建了首个四足机器人跑酷导航数据集（Quadruped Parkour Navigation Dataset，QPND），这一多模态数据集涵盖多样的导航行为与复杂地形。大量仿真及真实世界实验表明，MulDP可实现鲁棒的长时程自主导航，能有效穿越复杂地形。
英文摘要
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.
发表机构
- College of Intelligent Robotics and Advanced Manufacturing, Fudan University（复旦大学智能机器人与先进制造学院）
机构由 AI 辅助整理，请以论文原文为准。
