Physics-Aware Object-Centric SE(3) Forecasting for Real-Time
Overview
Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: 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 s…
Generated from attributed reports · Updated 1 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · execution time: 15 other · Basis not reported
“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.”
Exact source · revision 1Source owner not reported
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsPhysics-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.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).