ロボット計画のための構造化4D潜在予測モデル
Structured 4D Latent Predictive Model for Robot Planning
ロボットの計画に使うため、観測とテキスト指示に基づいてシーンの3D構造の進化を潜在空間で予測するモデルを提案し、将来のシーンを生成して行動に変換する。
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？
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5. 議論はある？
6. 次に読むべき論文は？
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著者: Zhiyi Li, Peilin Wu, Xiaoshen Han, Ruojin Cai, Yilun Du
分類: cs.RO, cs.CV
原文アブストラクト
Video predictive models are emerging as a powerful paradigm in robotics, offering a promising path toward task generalization, long-horizon planning, and flexible decision-making. However, prevailing approaches often operate on 2D video sequences, inherently lacking the 3D geometric understanding necessary for precise spatial reasoning and physical consistency. We introduce a Structured 4D Latent Predictive Model, which predicts the evolution of a scene's 3D structure in a structured latent space conditioned on observations and textual instructions. Our representation encodes the scene holistically and can be decoded into diverse 3D formats, enabling a more complete and 3D consistent scene understanding. This structured 4D latent predictive model serves as a planner, generating future scenes that are translated into executable actions by a goal-conditioned inverse dynamics module. Experiments demonstrate that our model generates futures with strong visual quality, substantially better 3D consistency and multi-view coherence compared to state-of-the-art video-based planners. Consequently, our full planning pipeline achieves superior performance on complex manipulation tasks, exhibits robust generalization to novel visual conditions, and proves effective on real-world robotic platforms. Our website is available at https://structured-4d-model.github.io/.
