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：利用仿人机械手在人类手部不可用或不适用的情况下辅助用户完成抓取任务，已变得日益重要。本文提出一项名为“辅助人类灵巧抓取”的新任务，旨在训练一种策略，通过控制机械手手指来协助用户抓取物体。与传统灵巧抓取不同，该任务因策略需在适应物体几何形状的同时，应对多样化的用户意图而更具挑战性。为应对这一挑战，我们提出一种包含两个子模块的方法：一个名为“抓取梯度场”（GraspGF）的手-物体条件化抓取基元，以及一个历史条件化残差策略。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“何时”以及以何种速度执行抓取动作。实验结果表明，我们所提方法在用户感知能力及实际应用实用性方面均优于基线方法。代码与演示视频可参见“https://sites.google.com/view/graspgf”。