The use of anthropomorphic robotic hands for assisting individuals in situations where human hands may be unavailable or unsuitable has gained significant importance. In this paper, we propose a novel task called human-assisting dexterous grasping that aims to train a policy for controlling a robotic hand's fingers to assist users in grasping objects. Unlike conventional dexterous grasping, this task presents a more complex challenge as the policy needs to adapt to diverse user intentions, in addition to the object's geometry. We address this challenge by proposing an approach consisting of two sub-modules: a hand-object-conditional grasping primitive called Grasping Gradient Field~(GraspGF), and a history-conditional residual policy. GraspGF learns `how' to grasp by estimating the gradient from a success grasping example set, while the residual policy determines `when' and at what speed the grasping action should be executed based on the trajectory history. Experimental results demonstrate the superiority of our proposed method compared to baselines, highlighting the user-awareness and practicality in real-world applications. The codes and demonstrations can be viewed at "https://sites.google.com/view/graspgf".

翻译：摘要：利用类人机器人手在人类手部不可用或不适宜的场景中辅助用户，已变得日益重要。本文提出一种名为“辅助性灵巧抓取”的新任务，旨在训练一个策略，以控制机器人手的手指辅助用户抓取物体。与传统灵巧抓取不同，该任务的挑战更为复杂：策略不仅需适应物体几何形状，还需适应多样化的用户意图。为应对这一挑战，我们提出由两个子模块组成的方法：一种是手-物体条件性的抓取基元，称为抓取梯度场（GraspGF）；另一种是历史条件性的残差策略。GraspGF通过从成功抓取样本集中估计梯度来学习“如何”抓取，而残差策略则根据轨迹历史决定抓取动作“何时”执行及其执行速度。实验结果表明，相较于基线方法，我们的方法具有优越性，并凸显了其在真实应用中的用户感知与实用性。相关代码与演示视频可访问“https://sites.google.com/view/graspgf”查看。