让人形机器人在复杂地形上稳定行走并避障
GuideWalk: Learning Unified Autonomous Navigation and Locomotion for Humanoid Robots across Versatile Terrains
- 导航与步态分离设计，实现动态避障与地形适应
- 通过教师蒸馏和强化学习提升策略鲁棒性
- 适合需要跨地形自主移动的机器人研究
人形机器人虽具备较强行走能力，但在多样地形上的可靠导航仍具挑战，因避障需与动态可行运动协同。本文提出GuideWalk，一种统一的端到端框架，将可通行性感知导航引导与地形自适应步态教学相结合。具体而言，引入导航模块提供显式速度引导，将避障与地形条件解耦，实现多样化环境下的鲁棒规划。提出复合教师蒸馏方案，聚合目标导向指令与动态一致动作，并凝练为单一策略。为进一步增强鲁棒性，采用强化学习与辅助行为克隆目标优化蒸馏策略，促进探索同时保留教师优良行为。实验表明，GuideWalk在保持人形机器人稳定步态的同时，实现了稳定有效的导航。
原文摘要 · Abstract (English)
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, we present GuideWalk, a unified end-to-end framework that integrates traversability-aware navigation guidance with terrain-adaptive locomotion teacher for humanoid navigation. Specifically, we introduce a navigation module that provides explicit velocity guidance, decoupling obstacle avoidance from terrain conditions to enable robust planning across diverse environments. We propose a composite teacher distillation scheme, where goal-directed commands and dynamically consistent actions are aggregated and distilled into a single policy. To further improve robustness, the distilled policy is refined with reinforcement learning and an auxiliary behavior cloning objective, which promotes exploration while preserving desirable teacher behaviors. Experiments demonstrate that GuideWalk achieves stable and effective navigation while maintaining stable humanoid locomotion.
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