Task-Conditioned Uncertainty Costmaps for Legged Locomotion
Kartikeya Singh, Christo Aluckal, Romeo Orsolino, Karthik Dantu
TLDR
This paper introduces task-conditioned uncertainty costmaps for legged robots, improving path planning reliability and reducing feasibility errors.
Key contributions
- Models epistemic uncertainty in learned foothold predictions for legged robots on unstructured terrain.
- Distinguishes in-distribution from out-of-distribution operating regimes using terrain observations and commanded motion.
- Integrates learned uncertainty into a unified costmap for uncertainty-aware path planning.
- Achieves up to 37% reduction in simulation feasibility error and more reliable planning than geometry-only baselines.
Why it matters
This work addresses a critical challenge in legged robotics: reliable navigation on complex, unknown terrains. By explicitly modeling uncertainty, robots can make safer, more informed planning decisions, especially when encountering novel environments. This improves robustness and expands the operational capabilities of autonomous legged systems.
Original 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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