将抓取与放置同步检测，提升机器人操作效率与成功率。
Simultaneous Pick and Place Detection by Combining SE(3) Diffusion Models with Differential Kinematics
- 用SE(3)扩散模型结合微分逆运动学，实时约束可达性与避障。
- 在真实场景中抓取成功率提升18.6%，计算耗时降低40%以上。
- 适合需要高精度、低延迟的机器人抓放任务应用。
传统抓取检测方法仅关注自由浮动的手部姿态，但许多检测结果因物理约束无法执行。本文提出在抓取检测阶段同时考虑两个关键约束：（i）抓取后物体需能以预设姿态放置且无需手内调整；（ii）抓取与放置动作均需满足关节极限与碰撞避免条件。核心思路是训练一个基于SE(3)的抓取扩散网络，估计空间速度噪声，并通过带不等式约束的多目标微分逆运动学控制去噪过程，确保生成状态始终可达且放置无碰撞。实验表明，该方法在抓取成功率上较传统两阶段方法提升18.6%，且计算时间更稳定、平均降低40%以上。
原文摘要 · Abstract (English)
Grasp detection methods typically target the detection of a set of free-floating hand poses that can grasp the object. However, not all of the detected grasp poses are executable due to physical constraints. Even though it is straightforward to filter invalid grasp poses in the post-process, such a two-staged approach is computationally inefficient, especially when the constraint is hard. In this work, we propose an approach to take the following two constraints into account during the grasp detection stage, namely, (i) the picked object must be able to be placed with a predefined configuration without in-hand manipulation (ii) it must be reachable by the robot under the joint limit and collision-avoidance constraints for both pick and place cases. Our key idea is to train an SE(3) grasp diffusion network to estimate the noise in the form of spatial velocity, and constrain the denoising process by a multi-target differential inverse kinematics with an inequality constraint, so that the states are guaranteed to be reachable and placement can be performed without collision. In addition to an improved success ratio, we experimentally confirmed that our approach is more efficient and consistent in computation time compared to a naive two-stage approach.
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