Learning Safe Humanoid Navigation from Reduced Order Models

2026-09-16

AI Summary

研究通过将导航问题分解为两部分，并使用简化模型训练策略，再结合全尺寸人形机器人动态，解决了复杂多层地形中的人形机器人导航难题。

Abstract

Research in humanoid robotics has achieved rapid progress in locomotion, and recent results have pushed the boundary on autonomous navigation. We demonstrate that a standard single-stage RL navigation pipeline struggles to scale to multi-level and multi-story terrain, limited by the difficulty of complex humanoid terrain interactions such as stairs. To overcome this challenge, we decompose the navigation problem into two pieces. First, we train a policy operating on the reduced order dynamics but with full 3D LiDAR observations to navigate complex, multi-story terrain. We then utilize this navigation knowledge to kickstart a policy operating on the full-order humanoid dynamics, with a frozen locomotion policy in the loop. Additionally, we demonstrate that applying a Poisson safety filter to the navigation policy output recovers safety in the presence of out-of-distribution obstacles, without dropping navigation success rate. We demonstrate the resulting RoM-Nav policy on a Unitree G1, accomplishing mapless multi-floor navigation covering trials with over 10m of vertical displacement and over 100m of path length. Project page with videos https://wdc3iii.github.io/rom-nav/ .

Problem

• Humanoid Robotics
• Autonomous Navigation
• Multi-level Terrain
• Stairs
• Reinforcement Learning

Innovation

• Reduced Order Dynamics
• Poisson Safety Filter
• Multi-story Terrain Navigation