RoboDream: スケーラブルなロボットデータ合成のための構成的世界モデル
RoboDream: Compositional World Models for Scalable Robot Data Synthesis
ロボット学習のための大規模データ生成を実現する、エンボディメント中心の世界モデルを提案。実ロボットの動作をレンダリングし、シーンや物体の事前知識を条件付けすることで、物理的に実現可能なフォトリアリスティックなデモを合成し、データスケーリングを可能にする。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Junjie Ye, Rong Xue, Basile Van Hoorick, Runhao Li, Harshitha Rajaprakash, Pavel Tokmakov, Muhammad Zubair Irshad, Vitor Guizilini, Yue Wang
分類: cs.RO, cs.CV
原文アブストラクト
Scaling robot learning requires large-scale, diverse demonstrations, yet real-world data collection via teleoperation remains prohibitively expensive and time-consuming. While video diffusion models offer a promising avenue for data scaling, existing generative approaches are often limited to superficial visual augmentation, or suffer from embodiment hallucinations that yield physically infeasible motions. We present a generalizable embodiment-centric world model that achieves scalable data generation by synthesizing photorealistic demonstrations with novel objects, in novel scenes, and from novel viewpoints. Our approach anchors generation to rendered robot motion while conditioning on explicit scene and object priors, effectively decoupling trajectory execution from environment synthesis. This formulation has the potential to unlock two powerful data scaling capabilities: (1) retrieval and rebirth, which repurposes existing trajectories into entirely new contexts without new motion data; and (2) prop-free teleoperation, where operators manipulate empty air and the model hallucinates the target objects and scene afterwards, eliminating reset time. We demonstrate with real-world experiments that our generated data consistently improves downstream policy performance and significantly reduces real-world data requirements across diverse manipulation tasks.
