Closing The Lab To Store Gap A Data Efficient Post

---
name: closing-the-lab-to-store-gap-a-data-efficient-post
description: 'Skill generated from arXiv paper 2607.20345: Closing the Lab-to-Store Gap: A Data-Efficient Post-Training and Experience-Driven Learning VLA Framework for Retail Humanoids'
metadata:
  {
    "arxiv": {
      "id": "2607.20345",
      "title": "Closing the Lab-to-Store Gap: A Data-Efficient Post-Training and Experience-Driven Learning VLA Framework for Retail Humanoids",
      "authors": ['Roger Sala Sisó', 'Tiago Silvério', 'Jakob Sand', 'Tran Nguyen Le'],
      "published": "2026-07-22",
      "categories": ['cs.RO', 'cs.AI'],
      "url": "https://arxiv.org/abs/2607.20345",
      "utility": 0.93
    }
  }
---
# Closing the Lab-to-Store Gap: A Data-Efficient Post-Training and Experience-Driven Learning VLA Framework for Retail Humanoids

**arXiv:** 2607.20345  
**Published:** 2026-07-22  
**Authors:** Roger Sala Sisó, Tiago Silvério, Jakob Sand, Tran Nguyen Le  
**Categories:** cs.RO, cs.AI  
**Utility:** 0.93

## Key Innovation
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...

## Potential Application
This paper presents advancements that could be applied to enhance agent capabilities in the areas of cs.RO, cs.AI.

## References
- arXiv: https://arxiv.org/abs/2607.20345