{
  "id": 12201,
  "url": "https://arxiv.org/abs/2607.18200v1",
  "title": "Learning Adaptive Safety Margins for Visual Navigation",
  "summary": "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",
  "authors": "Junyi Hu, Shuaihang Yuan, Geeta Chandra Raju Bethala, Anthony Tzes, Yi Fang",
  "category": "research",
  "topics": "bias-fairness,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-20T17:40:25.000Z",
  "fetched_at": "2026-07-21T05:10:12.656Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/12201",
  "original_url": "https://arxiv.org/abs/2607.18200v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}