{
  "id": 14086,
  "url": "https://arxiv.org/abs/2607.25895",
  "title": "HiFi-UMI: Learning Deployable Manipulation Policies from High-Fidelity UMI Data Alone",
  "summary": "Learning deployable manipulation policies is bottlenecked by the scarcity of data that is both high-fidelity and scalable. Real-robot teleoperation is accurate but costly to scale; robot-free UMI capture scales readily, and current practice uses the resulting data mainly for pre-training, adding a small real-robot \"anchor\" at post-training. We ask whether raising the fidelity of robot-free UMI data, rather than shrinking the real-robot fraction, can remove that anchor. We present HiFi-UMI, a por",
  "authors": "Simple AI, Yuteng Wei, Jinming Ma, Jiawei Wang, Weitao Zhou, Yushen Zuo",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-27T20:00:00.000Z",
  "fetched_at": "2026-07-29T05:10:12.205Z",
  "source_slug": "hf-daily",
  "source_name": "HuggingFace Daily Papers",
  "source_homepage": "https://huggingface.co/papers",
  "ethics_ai_record_url": "https://ethics.ai/record/14086",
  "original_url": "https://arxiv.org/abs/2607.25895",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}