The human hand's complex kinematics allow for simultaneous grasping and manipulation of multiple objects, essential for tasks like object transfer and in-hand manipulation. Despite its importance, robotic multi-object grasping remains underexplored and presents challenges in kinematics, dynamics, and object configurations. This paper introduces MultiGrasp, a two-stage method for multi-object grasping on a tabletop with a multi-finger dexterous hand. It involves (i) generating pre-grasp proposals and (ii) executing the grasp and lifting the objects. Experimental results primarily focus on dual-object grasping and report a 44.13% success rate, showcasing adaptability to unseen object configurations and imprecise grasps. The framework also demonstrates the capability to grasp more than two objects, albeit at a reduced inference speed.

翻译：人类手部复杂的运动学结构使得同时抓取和操控多个物体成为可能，这对于物体转移及手内操作等任务至关重要。尽管具有重要性，但机器人多物体抓取仍是一个研究不足的领域，并在运动学、动力学及物体配置方面存在挑战。本文提出MultiGrasp——一种基于多指灵巧手在桌面上进行多物体抓取的两阶段方法，包括：（i）生成预抓取建议，以及（ii）执行抓取并提起物体。实验结果主要聚焦于双物体抓取，成功率达44.13%，展示了该方法对未见过的物体配置及不精确抓取的自适应能力。该框架还展示出可抓取两个以上物体的能力，尽管推理速度有所降低。