高自由度机械臂手实现实时动态避障抓取
Hierarchical Reactive Grasping via Task-Space Velocity Fields and Joint-Space Quadratic Programming
- 分层设计：任务空间规划+关节空间优化
- 实测支持100+自由度系统实时响应
- 适合需要强适应性的机器人抓取场景
我们提出一种快速且具备反应能力的抓取框架，通过将任务空间速度场与关节空间二次规划（QP）在层级结构中结合。对于高自由度系统，全局运动规划面临状态维度和规划时长同时增加导致搜索空间组合爆炸的问题，使得实时规划难以实现。为此，我们在低维任务空间（如指尖位置）进行全局规划，并在全关节空间中局部追踪，同时满足所有约束。该方法通过构建多任务空间坐标（或部分关节坐标）的速度场，求解加权关节空间二次规划，以计算出优先级适配的关节速度来跟踪这些速度场。通过模拟实验及基于最新位姿追踪算法FoundationPose的真实世界测试，验证了该方法使高自由度臂手系统能够实现实时、无碰撞的到达动作，并能适应动态环境与外部扰动。
原文摘要 · Abstract (English)
We present a fast and reactive grasping framework that combines task-space velocity fields with joint-space Quadratic Program (QP) in a hierarchical structure. Reactive, collision-free global motion planning is particularly challenging for high-DoF systems, as simultaneous increases in state dimensionality and planning horizon trigger a combinatorial explosion of the search space, making real-time planning intractable. To address this, we plan globally in a lower-dimensional task space, such as fingertip positions, and track locally in the full joint space while enforcing all constraints. This approach is realized by constructing velocity fields in multiple task-space coordinates (or, in some cases, a subset of joint coordinates) and solving a weighted joint-space QP to compute joint velocities that track these fields with appropriately assigned priorities. Through simulation experiments and real-world tests using the recent pose-tracking algorithm FoundationPose, we verify that our method enables high-DoF arm-hand systems to perform real-time, collision-free reaching motions while adapting to dynamic environments and external disturbances.
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