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English(EN) TTT-Parkour: Rapid Test-Time Training for Perceptive Robot Parkour

人形机器人通过新的感知和训练框架获得跑酷技能

研究人员开发了两个新框架,使人形机器人能够高速导航复杂、非结构化的地形。第一个框架“野外徒步”使用感知跑酷框架,将深度和本体感觉直接映射到关节动作,在Unitree G1机器人上实现了高达2.5米/秒的速度。第二个框架“TTT-Parkour”在模拟环境中采用快速的测试时间训练,以微调特定、未见过的环境的策略,使机器人能够在大约10分钟内掌握具有挑战性的障碍物。 AI

影响 这些框架可以显著提升人形机器人在实际应用中的能力,使其在不可预测的环境中实现更敏捷、更适应性的运动。

排序理由 两篇研究论文详细介绍了人形机器人运动学的新框架。

在 arXiv cs.AI 阅读 →

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人形机器人通过新的感知和训练框架获得跑酷技能

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两篇研究论文详细介绍了人形机器人运动学的新框架。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Shaoting Zhu, Ziwen Zhuang, Mengjie Zhao, Kun-Ying Lee, Hang Zhao ·

    野外徒步:一种可扩展的感知跑酷框架,用于人形机器人

    arXiv:2601.07718v2 Announce Type: replace-cross Abstract: Achieving robust humanoid locomotion in complex, unstructured environments requires transitioning from reactive proprioception to proactive perception. However, integrating exteroception remains a significant challenge: ex…

  2. arXiv cs.AI TIER_1 English(EN) · Shaoting Zhu, Baijun Ye, Jiaxuan Wang, Jiakang Chen, Ziwen Zhuang, Linzhan Mou, Runhan Huang, Hang Zhao ·

    TTT-Parkour:感知机器人跑酷的快速测试时训练

    arXiv:2602.02331v2 Announce Type: replace-cross Abstract: Achieving highly dynamic parkour with humanoid robots on complex terrains unseen during the pre-training stage remains a challenge in robotics. Although general locomotion policies demonstrate capabilities across broad ter…