让四足机器人在视觉受扰时仍能稳定完成极限跑酷
REAL: Robust Extreme Agility via Spatio-Temporal Policy Learning and Physics-Guided Filtering
- 用多模态融合与时间记忆主动过滤视觉噪声
- 在1米视觉盲区下仍能完成极限障碍穿越，推理延迟仅13.1毫秒
- 适合高动态复杂环境下的机器人控制研究者
极限四足跑酷要求在高度动态条件下快速评估地形并精准落脚。尽管基于学习的系统已实现惊人敏捷性，但对感知退化仍极为脆弱——即使短暂的视觉噪声或延迟也会导致灾难性失败。为此，我们提出鲁棒极敏捷学习框架（REAL），实现感官受损下的可靠跑酷。REAL不依赖完美感知，而是紧密耦合视觉、本体感觉历史与时间记忆。通过将跨模态教师策略蒸馏为部署型学生模型，采用FiLM调制的Mamba骨干网络主动过滤视觉噪声并构建短期地形记忆。此外，物理引导的贝叶斯状态估计算法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，即使存在1米视觉盲区，也能成功穿越极端障碍，同时严格满足实时控制约束，推理延迟上限为13.1毫秒。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。