{
  "id": 14601093,
  "title": "Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance",
  "url": "https://urgent.news/2026/10/14/hierarchical-residual-policy-learning-for-real-world-mobile",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-14T17:59:52.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.20607v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Mobile Manipulation (MoMa) is a cornerstone of autonomous robotic systems, yet real-world fine-tuning remains a formidable challenge because of high-dimensional action spaces and intricate coupling between heterogeneous modules. In this paper, we propose HiRe-MoMa (Hierarchical Residual Mobile Manipulation), a systematic framework for stable and sample-efficient real-world fine-tuning of complex…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}