aiqbench analysis
UniReLo advances humanoid autonomy by unifying fall recovery and locomotion into a single policy that maintains stability across heterogeneous terrains without relying on fixed-threshold switching. By leveraging terrain-conditioned guidance to evaluate support feasibility, the method prevents dynamically fragile states often seen in posture-centered approaches during resumption of movement. This research represents a significant step toward robust whole-body control for humanoids operating in unstructured real-world environments.
UniReLo: Learning a Unified Humanoid Policy from Fall Recovery to Locomotion across Diverse Terrains
arXiv:2606.08922v3 Announce Type: replace 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, it takes advantage of terrain-conditioned recovery guidance to evaluate the evolving support state using a terrain-relative support representation and complementary support-feasibility factors. Simulation and outdoor real-world experiments demonstrate that UniReLo can deliver stable and continuous recovery-to-locomotion behaviors for humanoids across diverse field terrains.
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, it takes advantage of terrain-conditioned recovery guidance to evaluate the evolving support state using a terrain-relative support representation and complementary support-feasibility factors. Simulation and outdoor real-world experiments demonstrate that UniReLo can deliver stable and continuous recovery-to-locomotion behaviors for humanoids across diverse field terrains.
Robotics (cs.RO)
arXiv:2606.08922 [cs.RO]
(or arXiv:2606.08922v3 [cs.RO] for this version)
Full text under the publisher's open licence · 170 words
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