DREAM: 実世界からシミュレーションへの変換による展開時デモ生成でスケーラブルなポリシー適応を実現
DREAM: Deployment-Time Demonstration Generation via Real-to-Sim for Scalable Policy Adaptation
新しい作業環境でVLAモデルを微調整するためのデモデータを、人間の遠隔操作なしに自動生成するフレームワークDREAMを提案。実環境を再構築し、言語指示からタスク目標を自動翻訳して軌道を計画し、シミュレーションでデータを増強してVLAを微調整する。
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2. 先行研究と比べてどこがすごい？
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著者: Makoto Sato, Tatsuya Matsushima, Yutaka Matsuo, Yusuke Iwasawa
分類: cs.RO
原文アブストラクト
Vision-language-action (VLA) models have made strong progress in language-conditioned robot manipulation, but improving their performance in a new workspace still often requires action-labeled data from that environment. Collecting such data by human teleoperation is costly, especially when each workspace, object arrangement, or task may require new demonstrations. We present DREAM, a framework that generates fine-tuning data for a pretrained VLA from a captured workspace and a language instruction, without requiring a task-specific human demonstration. DREAM reconstructs the workspace, automatically translates the instruction into symbolic task goals and success criteria using a large language model, and uses task-and-motion planning to generate feasible robot trajectories. The planned trajectories are augmented across randomized object configurations, verified by the generated success criteria, and rendered into image-action examples for VLA fine-tuning. Through real-robot experiments on language-conditioned manipulation tasks, we study whether DREAM can serve as a scalable data-collection system for the deployment workspace by examining whether fine-tuning on its automatically generated data improves success over direct deployment and how its data-collection cost compares with human teleoperation when adapting a VLA to a new workspace.