统一学习跌倒恢复与行走，适配复杂地形。
UniReLo: Learning a Unified Humanoid Policy from Fall Recovery to Locomotion across Diverse Terrains
- 分层动态调控恢复与行走的运动先验，避免切换阈值
- 支持状态评估实现地形适应性恢复，实测稳定过渡
- 适合需要野外自主运行的人形机器人研发
可靠的跌倒恢复对人形机器人在非结构化野外环境中的自主运行至关重要。现有基于姿态的方法虽能生成多样跌倒姿态下的协调全身恢复动作，但可能导致动态脆弱的支撑状态，引发二次失衡或命令行走时不稳定，尤其在依赖地形的接触条件下。我们提出一种跨异构野外地形的统一人形策略（UniReLo），从跌倒恢复到行走全程建模。UniReLo利用连续门控的多尺度运动先验，根据恢复进度动态调节帧、序列和步态级对抗监督，保持恢复与行走的时序结构差异，无需固定阈值切换。此外，地形条件恢复引导通过地形相对支撑表示和支撑可行性评估，实时评估支撑状态演化。仿真与真实室外实验表明，UniReLo可在多种野外地形上实现稳定、连续的恢复至行走行为。补充视频见 https://vsislab.github.io/UniReLo/。
原文摘要 · Abstract (English)
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/.
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