The intricate kinematics of the human hand enable simultaneous grasping and manipulation of multiple objects, essential for tasks such as object transfer and in-hand manipulation. Despite its significance, the domain of robotic multi-object grasping is relatively unexplored and presents notable challenges in kinematics, dynamics, and object configurations. This paper introduces MultiGrasp, a novel two-stage approach for multi-object grasping using a dexterous multi-fingered robotic hand on a tabletop. The process consists of (i) generating pre-grasp proposals and (ii) executing the grasp and lifting the objects. Our experimental focus is primarily on dual-object grasping, achieving a success rate of 44.13%, highlighting adaptability to new object configurations and tolerance for imprecise grasps. Additionally, the framework demonstrates the potential for grasping more than two objects at the cost of inference speed.

翻译：人手复杂的运动学机制使得同时抓取与操控多个物体成为可能，这在物体转移及手内操控等任务中至关重要。尽管意义重大，机器人多物体抓取领域的研究仍相对匮乏，且在运动学、动力学及物体构型方面面临显著挑战。本文提出MultiGrasp——一种基于灵巧多指机械手在桌面上执行多物体抓取的两阶段创新方法。该流程包括：（i）生成预抓取提议；（ii）执行抓取并提起物体。实验重点聚焦于双物体抓取，成功率达44.13%，展现了该方法对新物体构型的适应能力以及对非精确抓取的容错性。此外，该框架还展现出抓取两个以上物体的潜力，但代价是推理速度的降低。