Learning to Navigate Under Imperfect Perception: Conformalised Segmentation for Safe Reinforcement Learning

• URL: http://arxiv.org/abs/2510.18485v1
• Date: Tue, 21 Oct 2025 10:07:04 GMT
• Title: Learning to Navigate Under Imperfect Perception: Conformalised Segmentation for Safe Reinforcement Learning
• Authors: Daniel Bethell, Simos Gerasimou, Radu Calinescu, Calum Imrie,
• Abstract summary: COPPOL is a conformal-driven perception-to-policy learning approach.<n>It integrates distribution-free, finite-sample safety guarantees into semantic segmentation.<n>It achieves near-complete detection of unsafe regions while reducing hazardous violations during navigation.
• Score: 6.255435016547602
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Reliable navigation in safety-critical environments requires both accurate hazard perception and principled uncertainty handling to strengthen downstream safety handling. Despite the effectiveness of existing approaches, they assume perfect hazard detection capabilities, while uncertainty-aware perception approaches lack finite-sample guarantees. We present COPPOL, a conformal-driven perception-to-policy learning approach that integrates distribution-free, finite-sample safety guarantees into semantic segmentation, yielding calibrated hazard maps with rigorous bounds for missed detections. These maps induce risk-aware cost fields for downstream RL planning. Across two satellite-derived benchmarks, COPPOL increases hazard coverage (up to 6x) compared to comparative baselines, achieving near-complete detection of unsafe regions while reducing hazardous violations during navigation (up to approx 50%). More importantly, our approach remains robust to distributional shift, preserving both safety and efficiency.

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