Learning Agile Navigation in Crowded Environments for Quadruped Robots

• URL: http://arxiv.org/abs/2607.15036v1
• Date: Thu, 16 Jul 2026 14:16:19 GMT
• Title: Learning Agile Navigation in Crowded Environments for Quadruped Robots
• Abstract summary: VOP-Nav is a novel navigation system for quadruped robots.<n>It combines the geometric safety of Velocity Obstacles with the agile adaptability of end-to-end learning.<n>VOP-Nav achieves higher success rates than all baselines while balancing speed and collision avoidance.
• Score: 19.29089241882416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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