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Evidence record 12677 · automatically gathered

Closing the Lab-to-Store Gap: A Data-Efficient Post-Training and Experience-Driven Learning VLA Framework for Retail Humanoids

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

Record details

Published: 22 July 2026
Source: arXiv
Category: Research
Topics: Agents & autonomy · Environment
Retrieved: 23 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Agents & autonomy · Shared topic: Environment

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Shared topic: Agents & autonomy · Shared topic: Environment

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Shared topic: Agents & autonomy · Shared topic: Environment

OpenForgeRL: Train Harness-native Agents in Any Environment

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How to cite this record

ethics.ai (22 July 2026), “Closing the Lab-to-Store Gap: A Data-Efficient Post-Training and Experience-Driven Learning VLA Framework for Retail Humanoids,” evidence record 12677, https://ethics.ai/record/12677 (originally published by arXiv).

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