Learning Adaptive Safety Margins for Visual Navigation

Robots in cluttered indoor spaces often fail not because they cannot generate collision-free paths, but because a fixed safety margin is mis-calibrated: conservative margins cause detours and timeouts, while permissive margins lead to near-boundary shortcuts under perception bias. Diffusion-based planners propose diverse trajectory candidates from egocentric RGB-D, yet reliable selection remains the bottleneck. We propose a context-conditioned safety critic that learns an adaptive clearance pref

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ethics.ai (20 July 2026), “Learning Adaptive Safety Margins for Visual Navigation,” evidence record 12201, https://ethics.ai/record/12201 (originally published by arXiv cs.AI).

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