ラボから店舗へのギャップを埋める：小売ヒューマノイド向けデータ効率的なポストトレーニングと経験駆動学習VLAフレームワーク
Closing the Lab-to-Store Gap: A Data-Efficient Post-Training and Experience-Driven Learning VLA Framework for Retail Humanoids
スーパーマーケットの棚補充タスクにおいて、データ効率的なポストトレーニングと経験駆動の学習を組み合わせたVLAフレームワークを提案し、単一GPUで実世界運用可能な性能を達成した。
詳しい要約
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著者: Roger Sala Sisó, Tiago Silvério, Jakob Sand, Tran Nguyen Le
分類: cs.RO, cs.AI
原文アブストラクト
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 three key components: (1) a data-efficient post-training pipeline with control-frequency alignment, data curation, task-relevant visual highlighting, and reduced VLA dependence; (2) a real-world study of experience-driven refinement, adapted from RECAP via a text-based advantage prefix and a vision-language value function; and (3) a latent-space analysis tool for studying in- and out-of-distribution behavior. Our results suggest that bridging the lab-to-store gap is primarily a systems integration challenge rather than an architectural one: careful data design and targeted post-training can transform a policy that fails under naive fine-tuning into a competent real-world system using only a single GPU.
