让机器人根据自身能力判断地形是否可走，避免误判。
Towards Capability-Aware Traversability Navigation for Unstructured Environments
- 将机器人物理限制直接嵌入特征空间，实现能力感知导航。
- 在真实轨迹上提升AUROC 11.0%，AUPRC 15.8%，优于最强基线。
- 适用于四足与轮式机器人，可在4.8 Hz下部署于嵌入式硬件。
在非结构化环境中评估可通行性需考虑机器人本体特性，相同地形对不同平台可能一可一不可。现有方法常通过后期轨迹筛选跨形态迁移预测，而非在学习表征中编码平台约束。本文提出能力感知可通行性（CAT）框架，通过交互式标注流程将密集监督掩码与物理轨迹对齐，并利用空间自适应去归一化（SPADE）模块以机器人特异性可通行向量调制语义地形图。在人工标注且轨迹对齐的数据集上，CAT在所有基于排名的指标中领先，于实际执行轨迹上使AUROC提升11.0%，于人工标注轨迹上使AUPRC提升15.8%。消融实验表明，空间条件化与每机器人的原型设计使模型具备超越通用路径预测的能力敏感性。在四足腿式机器人和轮式滑移转向机器人上的部署验证了其在嵌入式硬件上实现本体感知避障，运行频率达4.8 Hz。
原文摘要 · Abstract (English)
Estimating traversability in unstructured environments requires conditioning on robot embodiment, as the same terrain can be traversable for one platform and unsafe for another. Existing methods often transfer predictions across morphologies through late-stage trajectory filtering rather than encoding platform constraints in the learned representation. We propose Capability-Aware Traversability (CAT), a framework that embeds physical limits directly into the spatial feature space. CAT grounds dense supervision masks in physical trajectories through an interactive annotation pipeline and modulates semantic terrain maps with robot-specific traversability vectors through Spatially-Adaptive Denormalization (SPADE) blocks. Across human-annotated and trajectory-aligned datasets, CAT leads all ranking-based metrics, improving AUROC by 11.0% on physically executed trajectories and AUPRC by 15.8% on human traces over the strongest baseline. Ablations show that spatial conditioning and per-robot prototypes produce capability sensitivity beyond generic path prediction. Deployments on a legged quadruped and a wheeled skid-steer demonstrate embodiment-aware obstacle avoidance on embedded hardware at 4.8 Hz.
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