Task-Conditioned Uncertainty Costmaps for Legged Locomotion

arXiv cs.RO / 5/4/2026

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Key Points

The paper proposes task-conditioned uncertainty costmaps for legged robots, aiming to improve feasibility-aware motion planning on highly unstructured terrain.
By modeling epistemic uncertainty in learned foothold predictions conditioned on terrain observations and commanded motion, the method can distinguish in-distribution from out-of-distribution operating regimes.
A single learned model trained on limited data can output uncertainty reflecting missing training coverage, enabling detection of OOD regions during navigation.
The detected OOD uncertainty is integrated into a unified costmap-generation framework, and experiments in simulation and real-world tests show up to a 37% reduction in simulation feasibility error.
Compared with geometry-only baselines, the uncertainty-aware costmaps yield more reliable planning behavior across both in-distribution and OOD terrains.

Abstract

Legged robots maintain dynamic feasibility through multicontact interactions with terrain. Learned foothold prediction can provide feasibility-aware costs for motion planning and path selection, but accurately predicting future contacts from perceptual inputs such as height scans remains challenging on highly unstructured terrain, even with a repetitive gait cycle. In this work, we show that modeling epistemic uncertainty in predicted footholds, conditioned on terrain observations and commanded motion, distinguishes in-distribution from out-of-distribution operating regimes in simulation and real-world settings. This allows a single learned model, trained on limited data distributions, to express uncertainty caused by missing training coverage. We use this learned uncertainty to detect OOD regions and incorporate them into a unified costmap-generation framework for uncertainty-aware path planning. Using these uncertainty-aware costmaps, we evaluate feasibility error across in-distribution and OOD terrains in simulation and real-world settings. The results show improved OOD detection, up to a 37% reduction in simulation feasibility error, and more reliable planning behavior than geometry-only baselines.