外观
论文arXiv 2607.150363 个月前
面向四足机器人的拥挤环境敏捷导航学习
提出VOP-Nav，将VO几何安全与端到端学习结合，并在Isaac Gym和Unitree Go2实机验证拥挤动态导航。
宇树 Go2原文链接：Learning Agile Navigation in Crowded Environments for Quadruped Robots（arXiv 2607.15036 · 发布日期：2026-07-16）
Learning Agile Navigation in Crowded Environments for Quadruped Robots
提出VOP-Nav，将VO几何安全与端到端学习结合，并在Isaac Gym和Unitree Go2实机验证拥挤动态导航。
核心要点
- 动态拥挤环境中，严重传感器遮挡和人类运动不可预测使四足机器人导航面临挑战。
- VOP-Nav仅用本地机载观测，避免显式障碍检测与跟踪，结合VO安全性和端到端学习适应性。
- VOP-Net处理多帧LiDAR数据，隐式编码动态约束并预测源自VO理论的安全速度区域。
- Isaac Gym评估中成功率高于所有基线，并平衡移动速度与避碰；实机部署验证鲁棒性和效率。
验证范围与局限：验证范围为Isaac Gym仿真及Unitree Go2在复杂室内外动态环境中的实机部署。
英文摘要 (Abstract)
Navigating dynamic and crowded environments presents significant challenges for quadruped robots due to severe sensor occlusion and unpredictable human motion. Existing approaches face a trade-off: model-based methods, such as Velocity Obstacles (VO), theoretically guarantee safety but rely on accurate obstacle motion estimates that often fail in dense crowds, while end-to-end learning methods offer robustness but lack motion prediction capability of obstacles, leading to collisions or conservative behaviors. To solve this, we propose VOP-Nav, a novel navigation system that combines the geometric safety of VO with the agile adaptability of end-to-end learning. Using only local onboard observations, our system avoids explicit obstacle detection and tracking pipelines. The VOP-Net processes multi-frame LiDAR data to implicitly encode dynamic constraints and predict a safe velocity region derived from Velocity Obstacle theory. Importantly, the VO predictions serve a dual role: they are used as input to the navigation policy during inference and as a reward signal during training to encourage safe motion. Evaluations in Isaac Gym demonstrate that VOP-Nav achieves higher success rates than all baselines while balancing locomotion speed and collision avoidance. Real-world deployment on a Unitree Go2 quadruped robot further validates the system's robustness and efficiency in complex indoor and outdoor dynamic environments.
作者：Shuyu Wu, Zeyu Liu, Tianbao Zhang, Fanxing Li, Fangyu Sun, Mingkang Xiong, Wei Xi, W K Yu, Danping Zou
来源
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