物理を考慮した物体中心SE(3)予測によるリアルタイム能動的動的把握
Physics-Aware Object-Centric SE(3) Forecasting for Real-Time Proactive Dynamic Grasping
短い姿勢履歴から物体の将来の剛体軌道をSE(3)で予測し、到達可能性を考慮した接触状態選択により動的な物体把持を実現する軽量フレームワークを提案。
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著者: Yirui Zhou, Hongli Xu, Ying Zheng, Yi Wang, Lap-Pui Chau
分類: cs.RO
原文アブストラクト
Proactive dynamic grasping requires robots to predict the future state at which physical contact will occur, rather than reacting to the currently observed object pose. But what future must the robot predict, and at what level of abstraction? We argue that contact-relevant rigid-body state in SE(3) is a compact task-sufficient abstraction for this problem: it directly determines where and when the robot should establish contact, without requiring visually complete future scenes. This insight leads to a lightweight object-centric forecast-then-select framework that predicts future rigid-body trajectories from short pose histories and uses a reachability-aware interception selector to choose feasible contact states under kinematic and timing constraints. Trained with randomized physics simulation over primitive shapes, the forecaster transfers to real objects once their metric geometry and SE(3) state histories are recovered. Experiments across simulation and real-world dynamic grasping tasks show that our framework improves the average success rate from \(24.5\%\) to \(62.0\%\) in simulation, and from \(15.3\%\) to \(67.3\%\) in real-world catching, conveyor, and turntable interception. The SE(3) forecaster runs in \(15\,\mathrm{ms}\), orders of magnitude faster than dense visual future prediction, enabling real-time contact decision making. Our results suggest that effective proactive grasping does not require imagining the entire future scene. Instead, predicting the compact physical future that determines contact provides an effective and practical abstraction for real-time dynamic grasping.