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：使用拟人化机械手在人类双手无法使用或不适合的情况下辅助用户完成任务正变得日益重要。本文提出一项名为"辅助人手灵巧抓取"的新任务，旨在训练控制机械手手指运动的策略以协助用户抓取物体。与传统灵巧抓取不同，该任务面临更复杂的挑战：策略除需适应物体几何特征外，还需适配多样化的用户意图。我们提出由两个子模块组成的解决方案：手-物条件化的抓取原语——抓取梯度场(GraspGF)，以及历史条件化的残差策略。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"执行及执行速度。实验结果表明，相比基线方法，所提方法具有显著优越性，展现了其在真实应用中的用户感知性与实用性。相关代码与演示可访问"https://sites.google.com/view/graspgf"。