Publikation
Mind Your Steps: A General Learning Framework for Accurate Humanoid Foothold Tracking
Alessandro Montenegro; Shihao Li; Puze Liu; Alberto Maria Metelli; Jan Peters
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2606.08253, Pages 1-17, arXiv, 2026.
Zusammenfassung
Enabling humanoid robots to operate in complex,
dynamic environments remains a critical challenge, fundamentally
limited by the ability to navigate robustly, safely, and accurately.
While reinforcement learning with velocity-commanded policies
has achieved remarkable robustness in humanoid locomotion, this
approach lacks explicit control of the foothold placement, leading
to unsafe behavior, such as stepping onto human feet, or imprecise
navigation, hindering the following manipulation task. Conversely,
explicit foothold-tracking policies offer a promising alternative by
directly being commanded with target foot poses. However, existing
approaches are often limited by unrealistic state assumptions,
compromising real-world deployment, or they are part of staged
pipelines, making them tied to specific downstream tasks. In this
work, we introduce a novel, lightweight framework for training
general-purpose 3D foothold-tracking policies. By dynamically
providing footstep support through a goal sampler, this method
enables the learned policy to be agnostic to specific terrains. Our
new target representation effectively mitigates challenges arising
in the real world, such as noisy and inaccurate pose estimation and
foot contact estimation. Designed for direct real-world transfer,
our policy acts as a standalone low-level controller that can be
seamlessly paired with various high-level foothold generators. We
demonstrate the effectiveness of our framework through extensive
experiments in simulation and in the real world. By coupling our
policy with different upstream planners, we achieve natural and
accurate locomotion in challenging settings, paving the way for
loco-manipulation tasks in complex environments.