物理整合的な物体間推論による高密度クラッタ環境のReal-to-Sim
Real-to-Sim for Highly Cluttered Environments via Physics-Consistent Inter-Object Reasoning
単視点RGB-D画像から、接触グラフと微分可能剛体シミュレーションを用いて物理的に整合した3Dシーンを再構成し、接触の多いマニピュレーションを可能にする手法を提案。
詳しい要約
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著者: Tianyi Xiang, Jiahang Cao, Sikai Guo, Guoyang Zhao, Andrew F. Luo, Jun Ma
分類: cs.RO
原文アブストラクト
Reconstructing physically valid 3D scenes from single-view observations is a prerequisite for bridging the gap between visual perception and robotic control. However, in scenarios requiring precise contact reasoning, such as robotic manipulation in highly cluttered environments, geometric fidelity alone is insufficient. Standard perception pipelines often neglect physical constraints, resulting in invalid states, e.g., floating objects or severe inter-penetration, rendering downstream simulation unreliable. To address these limitations, we propose a novel physics-constrained Real-to-Sim pipeline that reconstructs physically consistent 3D scenes from single-view RGB-D data. Central to our approach is a differentiable optimization pipeline that explicitly models spatial dependencies via a contact graph, jointly refining object poses and physical properties through differentiable rigid-body simulation. Extensive evaluations in both simulation and real-world settings demonstrate that our reconstructed scenes achieve high physical fidelity and faithfully replicate real-world contact dynamics, enabling stable and reliable contact-rich manipulation.
