Task-Conditioned Uncertainty Costmaps for Legged Locomotion
This work addresses the challenge of accurate foothold prediction in highly unstructured terrain, where existing methods often fail to ensure motion planning feasibility due to unreliable perception inputs. The authors propose a task-conditioned cognitive uncertainty modeling approach that integrates foothold prediction, out-of-distribution (OOD) detection, and uncertainty estimation within a unified framework to generate an uncertainty-aware cost map. This method effectively identifies OOD regions and quantifies uncertainty arising from perceptual deficiencies, even under limited training data. Experimental results demonstrate significant improvements in OOD detection performance in both simulation and real-world environments, with up to a 37% reduction in planning feasibility error compared to geometry-only baseline methods, thereby exhibiting superior reliability and robustness.