We consider the problem of closed-loop robotic grasping and present a novel planner which uses Visual Feedback and an uncertainty-aware Adaptive Sampling strategy (VFAS) to close the loop. At each iteration, our method VFAS-Grasp builds a set of candidate grasps by generating random perturbations of a seed grasp. The candidates are then scored using a novel metric which combines a learned grasp-quality estimator, the uncertainty in the estimate and the distance from the seed proposal to promote temporal consistency. Additionally, we present two mechanisms to improve the efficiency of our sampling strategy: We dynamically scale the sampling region size and number of samples in it based on past grasp scores. We also leverage a motion vector field estimator to shift the center of our sampling region. We demonstrate that our algorithm can run in real time (20 Hz) and is capable of improving grasp performance for static scenes by refining the initial grasp proposal. We also show that it can enable grasping of slow moving objects, such as those encountered during human to robot handover.

翻译：我们研究闭环机器人抓取问题，提出一种新型规划器，通过视觉反馈与不确定性感知的自适应采样策略（VFAS）实现闭环控制。在每次迭代中，我们的VFAS-Grasp方法通过对种子抓取生成随机扰动，构建候选抓取集。随后采用一种融合学习型抓取质量评估、评估不确定性及种子提议距离的双重机制，促进时序一致性。此外，我们提出两种优化采样效率的机制：基于历史抓取得分动态调整采样区域尺寸与采样点数量，并利用运动矢量场估计器偏移采样区域中心。实验证明，本算法可实现实时运行（20 Hz），通过优化初始抓取提议显著提升静态场景的抓取性能。该方法同时支持慢速移动物体的抓取（例如人机交接场景中的物体）。