机器人可快速适应新地形，10分钟内学会翻越复杂障碍
TTT-Parkour: Rapid Test-Time Training for Perceptive Robot Parkour
- 通过真实场景采集+快速仿真重建，实现测试时实时训练
- 10分钟内完成从环境捕捉到策略微调，可穿越楔形、窄梁等复杂障碍
- 适合需要快速适应未知复杂地形的机器人研究与应用
在未见过的复杂地形上实现高度动态的人形机器人跑酷仍具挑战。尽管通用运动策略在广泛地形分布上表现良好，但在任意高难度环境中仍显不足。为此，我们提出一种真实-仿真-真实框架，利用快速测试时训练（TTT）在新地形上显著提升机器人穿越极端复杂几何结构的能力。采用两阶段端到端学习：先在多样化的程序生成地形上预训练策略，再基于真实世界捕获的高保真网格进行快速微调。特别地，我们开发了基于RGB-D输入的前馈式高效高保真几何重建流水线，确保测试时训练过程的速度与质量。实验表明，TTT-Parkour使机器人能掌握楔形、尖桩、箱子、梯形及窄梁等复杂障碍物。整个捕获、重建与测试时训练流程在多数测试地形上耗时少于10分钟。大量实验显示，经过测试时训练后的策略展现出强大的零样本仿真到现实迁移能力。
原文摘要 · Abstract (English)
Achieving highly dynamic humanoid parkour on unseen, complex terrains remains a challenge in robotics. Although general locomotion policies demonstrate capabilities across broad terrain distributions, they often struggle with arbitrary and highly challenging environments. To overcome this limitation, we propose a real-to-sim-to-real framework that leverages rapid test-time training (TTT) on novel terrains, significantly enhancing the robot's capability to traverse extremely difficult geometries. We adopt a two-stage end-to-end learning paradigm: a policy is first pre-trained on diverse procedurally generated terrains, followed by rapid fine-tuning on high-fidelity meshes reconstructed from real-world captures. Specifically, we develop a feed-forward, efficient, and high-fidelity geometry reconstruction pipeline using RGB-D inputs, ensuring both speed and quality during test-time training. We demonstrate that TTT-Parkour empowers humanoid robots to master complex obstacles, including wedges, stakes, boxes, trapezoids, and narrow beams. The whole pipeline of capturing, reconstructing, and test-time training requires less than 10 minutes on most tested terrains. Extensive experiments show that the policy after test-time training exhibits robust zero-shot sim-to-real transfer capability.
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