  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.
