Towards Capability-Aware Traversability Navigation for Unstructured Environments
Researchers propose the Capability-Aware Traversability (CAT) framework, which embeds robot physical limits directly into the spatial feature space using an interactive annotation pipeline and Spatially-Adaptive Denormalization (SPADE) blocks. CAT achieves significant improvements in traversability estimation, with AUROC up by 11.0% and AUPRC up by 15.8% on benchmark datasets, and demonstrates real-time embodiment-aware obstacle avoidance at 4.8 Hz on legged and wheeled platforms.
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[Submitted on 22 Jul 2026]
Title:Towards Capability-Aware Traversability Navigation for Unstructured Environments
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Abstract: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.
Comments: 8 pages, 7 figures. Accepted to the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026). Project page: this https URL
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.20679 [cs.RO]
(or arXiv:2607.20679v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.20679
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Gianluca Capezzuto [view email] [v1] Wed, 22 Jul 2026 19:28:57 UTC (5,575 KB)
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