GuideWalk: Learning Unified Autonomous Navigation and Locomotion for Humanoid Robots across Versatile Terrains

Haoxuan Han, Chen Chen, Linao Gong, Xin Yang, Hao Hu, Junhong Guo, Zhicheng He, Yao Su, Fenghua He · N/A · 2026

Summary

Abstract Summary

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.

Key Points

Proposes a unified end-to-end framework combining traversability-aware navigation with terrain-adaptive locomotion.
Introduces velocity guidance that decouples obstacle avoidance from terrain conditions.
Uses composite teacher distillation followed by RL refinement and behavior cloning for robust policy learning.
Demonstrates stable navigation across versatile terrains on humanoid robots.

Accelerating and Scaling MPC-Guided Reinforcement Learning for Humanoid Locomotion and Manipulation

Junheng Li, Liang Wu, Sergio A. Esteban et al. · arXiv preprint · Jun 2026

In humanoid motion control, model predictive control (MPC) offers physically grounded prediction and constraint handling, while reinforcement learning (RL) enables robust whole-body skills through large-scale simulation. However, using MPC inside RL often requires time-consuming problem construction or excessive training overhead, making such frameworks difficult to justify in practice. This work studies efficient training-time MPC guidance for humanoid locomotion and manipulation, termed MPC-RL. We introduce a centroidal-dynamics MPC reward formulation that leverages guidance from MPC trajectories in training time. To make this practical in massively parallel RL, we develop π^nMPC, a parallel-in-horizon and construction-free batched GPU MPC solver that operates directly on time-varying dynamics to avoid high memory usage and pre-compilation. Through a variety of comparative studies and hardware validations, we have found that MPC-RL achieves superior performance in locomotion and manipulation skills.

humanoid reinforcement-learning model-predictive-control locomotion manipulation

Code PDF Advanced

GitHub ★ — Jun 2026

Actuator Reality Shaping for Zero-Shot Sim-to-Real Robot Learning

Satoshi Yamamori, Koji Ishihara, Kentaro Minamikawa et al. · arXiv preprint · Jul 2026

Sim-to-real transfer in robot learning is often limited by discrepancies between the ideal actuator dynamics assumed during policy training and the nonlinear, hardware-dependent be...

ar-vr humanoid locomotion

PDF Intermediate

No code repo Jul 2026

M3imic: Learning a Versatile Whole-Body Controller for Multimodal Motion Mimicking

Zuxing Lu, Ziang Zheng, Yao Lyu et al. · arXiv preprint · Jun 2026

Building a general-purpose whole-body controller is essential for enabling diverse motion capabilities in humanoid robots across a wide range of downstream tasks, including locomotion and loco-manipulation. Different tasks rely on distinct motion reference modalities: locomotion primarily depends on...

sim-to-real reinforcement-learning locomotion manipulation humanoid

Code PDF Advanced

GitHub ★ — Jun 2026

Stubborn: A Streamlined and Unified Reinforcement Learning Framework for Robust Motion Tracking and Fall Recovery for Humanoids

Xiao Ren, Yuhui Yang, Zongbiao Weng et al. · arXiv preprint · Jun 2026

Recent reinforcement learning approaches have shown great promise in improving humanoid motion tracking performance and achieving fall recovery under disturbances. However, most existing works treat motion tracking and fall recovery as different tasks and require multi-stage training with specialize...

humanoid reinforcement-learning locomotion