MulDP: Multimodal Diffusion Policy for Autonomous Quadruped Parkour Navigation across Complex Terrains

2026-09-03

AI Summary

为解决四足机器人自主跑酷导航中的速度调节、长期预测行为及感知与执行紧密结合的问题，提出了一种多模态扩散策略MulDP，并构建了QPND数据集支持训练。

Abstract

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.

Problem

• quadruped robots
• autonomous navigation
• complex terrains
• velocity regulation
• perception and control

Innovation

• Multimodal Diffusion Policy
• Autonomous Quadruped Navigation
• Complex Terrains
• QPND