Enabling multi-fingered robots to grasp and manipulate objects with human-like dexterity is especially challenging during the dynamic, continuous hand-object interactions. Closed-loop feedback control is essential for dexterous hands to dynamically finetune hand poses when performing precise functional grasps. This work proposes an adaptive motion planning method based on deep reinforcement learning to adjust grasping poses according to real-time feedback from joint torques from pre-grasp to goal grasp. We find the multi-joint torques of the dexterous hand can sense object positions through contacts and collisions, enabling real-time adjustment of grasps to generate varying grasping trajectories for objects in different positions. In our experiments, the performance gap with and without force feedback reveals the important role of force feedback in adaptive manipulation. Our approach utilizing force feedback preliminarily exhibits human-like flexibility, adaptability, and precision.

翻译：让多指机械手像人类一样灵巧地抓取和操作物体，在动态、连续的手-物交互过程中尤其具有挑战性。闭环反馈控制对于灵巧手在执行精确功能性抓取时动态微调手部姿态至关重要。本文提出一种基于深度强化学习的自适应运动规划方法，通过关节扭矩的实时反馈，在预抓取到目标抓取过程中调整抓取姿态。我们发现灵巧手多关节扭矩可通过接触和碰撞感知物体位置，从而实时调整抓取动作，为不同位置的物体生成多变的抓取轨迹。实验中，有/无力反馈的性能差异揭示了力反馈在自适应操作中的重要作用。我们的方法通过利用力反馈初步展现出类人般的灵活性、适应性和精确性。