用不确定性地图提升腿式机器人在复杂地形的规划可靠性
Task-Conditioned Uncertainty Costmaps for Legged Locomotion
- 基于地形和运动指令建模预测足点的置信度
- 实测显示未知区域检测准确率提升，仿真可行性误差降37%
- 适合做复杂环境移动机器人路径规划的研究者参考
腿式机器人通过与地形的多接触互动维持动态可行性。虽然学习足点预测可为运动规划提供可行性感知代价，但在高度结构化的地形上，仅凭高度扫描等感知输入准确预测未来接触仍具挑战性，即使具有重复步态周期。本文表明，将预测足点的认知不确定性（epistemic uncertainty）建模为地形观测和指令运动的条件函数，可在模拟与真实场景中区分分布内与分布外运行状态。这使得仅在有限数据分布上训练的单一模型，也能表达因训练覆盖不足导致的不确定性。我们利用该学习到的不确定性来检测分布外区域，并将其整合进统一的成本图生成框架，实现不确定性感知的路径规划。使用这些不确定性感知成本图，我们在模拟和真实场景中评估了分布内与分布外地形上的可行性误差。结果表明，分布外检测性能提升，仿真可行性误差最高降低37%，且规划行为比仅依赖几何信息的基线方法更可靠。
原文摘要 · Abstract (English)
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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