DynaSlots: スロット単位の3Dダイナミクス事前学習によるロボットマニピュレーション
DynaSlots: Slot-Centric 3D Dynamics Pretraining for Robot Manipulation
点群フレームを時間的に一貫した少数のスロットに分解し、シーン全体ではなくスロットごとにダイナミクスを学習するラベル不要の事前学習手法を提案。シミュレーションと実機で従来のシーンレベル手法を上回る成功率と頑健性を示した。
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？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Meng Li, Lijiang Chen, Qi Zhao, Jun Jiang, Longhao Zou, Shuchang Lyu, Guangliang Cheng
分類: cs.RO
原文アブストラクト
Robot manipulation involves localized changes: picking up a cup moves the cup while leaving the surrounding scene largely unchanged. Yet existing 3D visual pretraining methods, including recent dynamics-aware variants, model state transitions at the scene level, compressing the motion of everything in a scene into a single global latent action. This conflates semantically distinct transitions in scenes with multiple moving parts, obscures dependencies between changing regions, and leaves the learned representation vulnerable to task-irrelevant scene content. We introduce DynaSlots, an action-free, label-free pretraining framework that decomposes each point cloud frame into a small set of temporally consistent slots and models dynamics at this per-slot level rather than the scene level. A temporal slot propagation module promotes consistent slot assignments across frames; an inverse dynamics model then extracts one latent action per slot from feature differences, and a diffusion-based forward dynamics model, paired with a self-supervised interaction head, predicts each slot's future state while learning pairwise proximity-and-motion relations. Experiments on Meta-World, Adroit, and a RealMan RM65-B robot demonstrate that DynaSlots outperforms prior scene-level baselines. It achieves superior success rates in simulation and exhibits strong robustness to scene clutter and unseen objects in real-world manipulation. Code is available at \href{https://zhucezhanghao12345-source.github.io/DynaSlots-Anonymous/}{https://zhucezhanghao12345-source.github.io/DynaSlots-Anonymous/}.