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arXiv · 2605.00261

Task-Conditioned Uncertainty Costmaps for Legged Locomotion

Kartikeya Singh·Christo Aluckal·Romeo Orsolino·Karthik Dantu
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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.

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Kartikeya Singh, Christo Aluckal, Romeo Orsolino, Karthik Dantu. 2026-04-30. Task-Conditioned Uncertainty Costmaps for Legged Locomotion. https://arxiv.org/abs/2605.00261

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