UniReLo: Learning a Unified Humanoid Policy from Fall Recovery to Locomotion across Diverse Terrains

2026/06/30 by Xiaoyu Xu, Zhiming Chen, Yuenan Zhao +2
Computer Science · #cs.RO

paper · pdf

arxiv created 2026/07/31 · arxiv updated 2026/08/03

Abstract

Reliable fall recovery, which commonly aims at attaining a nominal upright posture, is essential for the autonomous operation of humanoid robots in unstructured field environments. Although existing posture-centered methods can synthesize coordinated whole-body recovery motions from diverse fallen configurations, they may result in a dynamically fragile support state, leading to secondary loss of balance or unstable resumption of commanded locomotion, particularly under terrain-dependent contact conditions. We propose to learn a unified humanoid policy from fall recovery to locomotion (UniReLo) across heterogeneous field terrains. UniReLo leverages continuously gated multi-scale motion priors to modulate frame-, sequence-, and gait-level adversarial supervision according to recovery progress, preserving the distinct temporal structures of recovery and locomotion without requiring fixed-threshold switching. In addition, terrain-conditioned recovery guidance evaluates the evolving support state using a terrain-relative support representation and support-feasibility assessment. Simulation and outdoor real-world experiments demonstrate that UniReLo can deliver stable and continuous recovery-to-locomotion behaviors for humanoids across diverse field terrains. The supplementary video is available at https://vsislab.github.io/UniReLo/.

Citations

• Unified Humanoid Fall-Safety Policy from a Few Demonstrations
• FR-Net: Learning Robust Quadrupedal Fall Recovery on Challenging Terrains through Mass-Contact Prediction
• Learning Perceptive Humanoid Locomotion over Challenging Terrain
• HiFAR: Multi-Stage Curriculum Learning for High-Dynamics Humanoid Fall Recovery
• Learning Humanoid Locomotion with World Model Reconstruction
• Learning Getting-Up Policies for Real-World Humanoid Robots
• Learning Humanoid Standing-up Control across Diverse Postures
• FRASA: An End-to-End Reinforcement Learning Agent for Fall Recovery and Stand Up of Humanoid Robots
• Learning Humanoid Locomotion over Challenging Terrain
• Advancing Humanoid Locomotion: Mastering Challenging Terrains with Denoising World Model Learning
• DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learning
• Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning
• Proximal Policy Optimization Algorithms

Related

• Unified Humanoid Fall-Safety Policy from a Few Demonstrations
• Learning Getting-Up Policies for Real-World Humanoid Robots
• Learning Humanoid Standing-up Control across Diverse Postures
• Humanoid Whole-Body Locomotion on Narrow Terrain via Dynamic Balance and Reinforcement Learning
• Reachability Aware Capture Regions with Time Adjustment and Cross-Over for Step Recovery