Humanoid robots have achieved strong locomotion capabilities, but reliable navigation on versatile terrains remains challenging because obstacle avoidance must be coordinated with dynamically feasible motion.
In this work, researchers present GuideWalk, a unified end-to-end framework that integrates traversability-aware navigation guidance with terrain-adaptive locomotion teacher for humanoid navigation.
Key innovations:
Navigation Module: Introduces explicit velocity guidance that decouples obstacle avoidance from terrain conditions, enabling robust planning across diverse environments.
Composite Teacher Distillation: Aggregates goal-directed commands and dynamically consistent actions, distilling them into a single policy.
Reinforcement Learning Refinement: Further improves robustness with RL and auxiliary behavior cloning objective, promoting exploration while preserving desirable teacher behaviors.
Experiments demonstrate that GuideWalk achieves stable and effective navigation while maintaining stable humanoid locomotion across various terrains.
The research was submitted to arXiv on June 9, 2026 as paper arXiv:2606.10449, authored by researchers from multiple institutions.
Source: arXiv (https://arxiv.org/abs/2606.10449)
