{
  "id": 12677,
  "url": "https://arxiv.org/abs/2607.20345v1",
  "title": "Closing the Lab-to-Store Gap: A Data-Efficient Post-Training and Experience-Driven Learning VLA Framework for Retail Humanoids",
  "summary": "Closing the gap between benchmark performance and reliable real-world operation remains a central challenge for Vision-Language-Action (VLA) humanoid robots, which must handle execution errors, distribution shifts, and environmental variability. This paper presents DEED (Data-Efficient Post-Training and Experience-Driven Learning), a systems-level approach evaluated on a supermarket chip-restocking task using a Unitree G1-Edu humanoid robot and the GR00T N1.6 foundation model. DEED comprises thr",
  "authors": "Roger Sala Sisó, Tiago Silvério, Jakob Sand, Tran Nguyen Le",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-22T16:30:51.000Z",
  "fetched_at": "2026-07-23T05:10:49.458Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/12677",
  "original_url": "https://arxiv.org/abs/2607.20345v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}