发表机构
University of São Paulo（圣保罗大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究非结构化环境中可通行性估计，提出能力感知可通行性（CAT）框架，将物理限制嵌入空间特征空间，通过交互式标注和SPADE块改进可通行性预测，在多数据集上领先，实现实例感知避障。
AI 中文摘要
在非结构化环境中估计可通行性需要考虑机器人的具体情况，因为相同地形对一个平台可能可通行，对另一个可能不安全。现有方法常通过后期轨迹滤波在不同形态间转移预测，而非在学习表示中编码平台约束。我们提出能力感知可通行性（CAT）框架，将物理限制直接嵌入空间特征空间。通过交互式标注管道在物理轨迹中确定密集监督掩码，并通过空间自适应反归一化（SPADE）块用特定于机器人的可通行性向量调制语义地形图。在多个数据集上CAT领先，在物理执行轨迹上AUROC提高11.0%，在人类轨迹上AUPRC提高15.8%。消融实验表明空间条件和每个机器人的原型产生了超越通用路径预测的能力敏感性。在腿式四足机器人和轮式滑移转向机器人上的部署展示了在嵌入式硬件上4.8Hz的实例感知避障。
英文摘要
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.
Comments8 pages, 7 figures. Accepted to the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026). Project page: https://capability-aware-traversability.github.io/
