A robot performing multi-object grasping needs to sense the number of objects
in the hand after grasping. The count plays an important role in determining
the robot's next move and the outcome and efficiency of the whole pick-place
process. This paper presents a data-driven contrastive learning-based counting
classifier with a modified loss function as a simple and effective approach for
object counting despite significant occlusion challenges caused by robotic
fingers and objects. The model was validated against other models with three
different common shapes (spheres, cylinders, and cubes) in simulation and in a
real setup. The proposed contrastive learning-based counting approach achieved
above 96\% accuracy for all three objects in the real setup.

基于对比学习的计数分类器是一种简单而有效的方法，在面对机器人手指和物体造成的显著遮挡挑战时进行目标计数，该方法在模拟和实际环境中验证后，在三种常见形状物体（球体、圆柱体和立方体）上能达到 96% 以上的准确率。

机械手中的物体计数

Counting Objects in a Robotic Hand

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.

该论文介绍了 MultiGrasp，一种用于多指灵巧机械手在桌面上进行多物体抓取的两阶段方法，包括生成抓前提案和执行抓取举起物体。实验结果主要集中在双物体抓取，并报告了 44.13% 的成功率，展示了对未见物体配置和不精确抓取的适应性。该框架还展示了抓取超过两个物体的能力，但推理速度较慢。