论文/arXiv 2610.17843·15 天前

面向实时主动动态抓取的物理感知物体中心 SE(3) 预测

面向主动动态抓取，提出以物体为中心的 SE(3) 预测与可达选择框架，并在仿真和真实动态抓取中验证。

AI 整理

• 本体 SDK 与控制
• 仿真与训练
• 数据与采集

原文链接：Physics-Aware Object-Centric SE(3) Forecasting for Real-Time Proactive Dynamic Grasping（arXiv 2610.17843 · 发布日期：2026-10-13）

Physics-Aware Object-Centric SE(3) Forecasting for Real-Time Proactive Dynamic Grasping

面向主动动态抓取，提出以物体为中心的 SE(3) 预测与可达选择框架，并在仿真和真实动态抓取中验证。

核心要点

• 主动动态抓取需预测发生物理接触时的未来状态，而非仅对当前物体位姿作反应。
• 将接触相关刚体 SE(3) 状态作为紧凑抽象，用短位姿历史预测未来刚体轨迹。
• 框架结合可达性感知拦截选择器，在运动学与时序约束下选择可行接触状态。
• 实验显示仿真成功率由24.5%升至62.0%，真实任务由15.3%升至67.3%。

验证范围与局限：验证范围为仿真及真实世界的 catching、conveyor 和 turntable interception 动态抓取任务。

英文摘要 (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.

作者：Yirui Zhou, Hongli Xu, Ying Zheng, Yi Wang, Lap‐Pui Chau

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2610.17843：Physics-Aware Object-Centric SE(3) Forecasting for Real-Time Proactive Dynamic Grasping访问 2026-10-21