Humans coordinate the abundant degrees of freedom (DoFs) of hands to dexterously perform tasks in everyday life. We imitate human strategies to advance the dexterity of multi-DoF robotic hands. Specifically, we enable a robot hand to grasp multiple objects by exploiting its kinematic redundancy, referring to all its controllable DoFs. We propose a human-like grasp synthesis algorithm to generate grasps using pairwise contacts on arbitrary opposing hand surface regions, no longer limited to fingertips or hand inner surface. To model the available space of the hand for grasp, we construct a reachability map, consisting of reachable spaces of all finger phalanges and the palm. It guides the formulation of a constrained optimization problem, solving for feasible and stable grasps. We formulate an iterative process to empower robotic hands to grasp multiple objects in sequence. Moreover, we propose a kinematic efficiency metric and an associated strategy to facilitate exploiting kinematic redundancy. We validated our approaches by generating grasps of single and multiple objects using various hand surface regions. Such grasps can be successfully replicated on a real robotic hand.

翻译：人类通过协调手部丰富的自由度（DoFs）在日常生活环境中灵巧地执行任务。我们模仿人类策略以提升多自由度机器人手的灵巧性，具体而言，通过利用机器人手的所有可控自由度（即运动学冗余）实现多物体抓取。我们提出一种类人抓取合成算法，该算法不局限于指尖或手掌内表面，可基于任意相对的手部表面区域生成具有成对接触点的抓取构型。为建模抓取所需的手部可用空间，我们构建了由所有指节及手掌的可达空间组成的可达性图谱。该图谱引导约束优化问题的构建，从而求解可行且稳定的抓取构型。我们设计迭代流程使机器人手能够顺序抓取多个物体。此外，提出运动学效率指标及相关策略以促进运动学冗余的利用。通过在不同手部表面区域生成单物体与多物体抓取构型验证了所提方法，且这些抓取构型可在真实机器人手上成功复现。