Learning Agile Navigation in Crowded Environments for Quadruped Robots

2026-07-16

AI Summary

This work addresses the challenge of enabling quadrupedal robots to navigate safely and agilely in dynamic, crowded environments where sensor occlusions and unpredictable human motion complicate perception and planning. The authors propose VOP-Net, a novel approach that uniquely integrates predictions from velocity obstacle theory directly into an end-to-end reinforcement learning framework—serving simultaneously as both input observations and reward signals. By leveraging only multi-frame local LiDAR data, VOP-Net implicitly models dynamic constraints and predicts safe velocity regions without requiring explicit obstacle detection or tracking. Evaluated in Isaac Gym simulations, the method achieves higher success rates than all baseline approaches and demonstrates robust, efficient performance on a Unitree Go2 robot operating in complex indoor and outdoor dynamic environments.

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.

Problem

• quadruped robots
• crowded environments
• navigation
• sensor occlusion
• obstacle motion prediction

Innovation

• Velocity Obstacle
• end-to-end learning
• quadruped navigation
• LiDAR-based perception
• safe motion planning