aiqbench analysis
The paper argues that a robot grasping a moving object only needs to forecast the object's rigid-body pose (SE(3)) at the moment of contact, not the full future scene. A small learned forecaster, trained on simulated primitive shapes, feeds a reachability-aware selector, and the authors report gains in success rate and a 15 ms inference time. The simulation and real-world results are promising, but they come from a limited set of tasks and objects, so readers should check how well the transfer holds for more varied or cluttered settings.
Physics-Aware Object-Centric SE(3) Forecasting for Real-Time Proactive Dynamic Grasping
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 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.
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 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.
Robotics (cs.RO)
arXiv:2610.17843 [cs.RO]
(or arXiv:2610.17843v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.17843 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 311 words
