Physics-Aware Object-Centric SE(3) Forecasting for Real-Time Proactive Dynamic Grasping
Physics-Aware Object-Centric SE(3) Forecasting for Real-Time Proactive Dynamic Grasping
This paper presents a lightweight object-centric framework for proactive dynamic grasping that predicts future rigid-body states in SE(3) to enable real-time contact decisions. The method improves success rates in both simulation and real-world tasks, demonstrating the effectiveness of compact physical abstractions over dense visual prediction.
Source: arXiv Robotics — research abstracts · Read original article ↗
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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.
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cs 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(
Open source S4
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.
Open source S5
arXiv:2610.17843v1 Announce Type: new Abstract: 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 d
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etermines 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 physi
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Source：arXiv Robotics — research abstracts · arxiv.org