{
  "id": 14496,
  "url": "https://arxiv.org/abs/2607.23565v1",
  "title": "Anticipatory Risk-Guided Reinforcement Learning for Safe Flight Through Dynamic Clutter",
  "summary": "Safe quadrotor navigation in cluttered and dynamic environments depends not only on instantaneous geometric perception, but more critically on anticipating collision risks induced by relative motion. Conventional modular pipelines frequently suffer from perception latency, while end-to-end learning methods relying on implicit scalar rewards often struggle to extract reliable spatio-temporal features without physics-grounded supervision. To address this, we propose an anticipatory risk-guided rei",
  "authors": "Yuchao Mei, Guohao Zhang, Luxia Ai, Haopeng Chen, Wenbing Tao",
  "category": "research",
  "topics": "environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-26T09:31:49.000Z",
  "fetched_at": "2026-07-29T05:10:12.205Z",
  "source_slug": "arxiv-cslg",
  "source_name": "arXiv cs.LG",
  "source_homepage": "https://arxiv.org/list/cs.LG/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/14496",
  "original_url": "https://arxiv.org/abs/2607.23565v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}