REAL：通过时空策略学习和物理引导过滤实现鲁棒极端敏捷
REAL: Robust Extreme Agility via Spatio-Temporal Policy Learning and Physics-Guided Filtering
AI总结：
REAL通过结合时空策略学习和物理引导过滤，提升在动态环境下极端敏捷的鲁棒性，能够在视觉干扰下实现可靠攀爬。
摘要：
极端四足机器人攀爬需要在高度动态条件下快速评估地形和精确放置脚部。尽管最近基于学习的系统实现了出色的敏捷性，但它们在感知退化下仍然脆弱，即使短暂的视觉噪声或延迟也会导致灾难性失败。为了解决这个问题，我们提出了鲁棒极端敏捷学习（REAL），一种端到端框架，用于在传感器退化下可靠的攀爬。REAL不依赖于完美清晰的感知，而是紧密耦合视觉、本体感觉历史和时间记忆。我们蒸馏了一个跨模态教师策略到一个可部署的学生中，该学生配备了FiLM调制的Mamba骨干网络，以主动过滤视觉噪声并构建短期地形记忆。此外，一个物理引导的贝叶斯状态估计器在高冲击动作中强制刚体一致性。在Unitree Go2四足机器人上验证，REAL即使在1米的视觉盲区下也能成功穿越极端障碍，同时严格满足实时控制约束，具有13.1毫秒的有界推理时间。
英文摘要：
Extreme legged parkour demands rapid terrain assessment and precise foot placement under highly dynamic conditions. While recent learning-based systems achieve impressive agility, they remain fundamentally fragile to perceptual degradation, where even brief visual noise or latency can cause catastrophic failure. To overcome this, we propose Robust Extreme Agility Learning (REAL), an end-to-end framework for reliable parkour under sensory corruption. Instead of relying on perfectly clean perception, REAL tightly couples vision, proprioceptive history, and temporal memory. We distill a cross-modal teacher policy into a deployable student equipped with a FiLM-modulated Mamba backbone to actively filter visual noise and build short-term terrain memory actively. Furthermore, a physics-guided Bayesian state estimator enforces rigid-body consistency during high-impact maneuvers. Validated on a Unitree Go2 quadruped, REAL successfully traverses extreme obstacles even with a 1-meter visual blind zone, while strictly satisfying real-time control constraints with a bounded 13.1 ms inference time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
