In this work, we describe a multi-object grasping benchmark to evaluate the grasping and manipulation capabilities of robotic systems in both pile and surface scenarios. The benchmark introduces three robot multi-object grasping benchmarking protocols designed to challenge different aspects of robotic manipulation. These protocols are: 1) the Only-Pick-Once protocol, which assesses the robot's ability to efficiently pick multiple objects in a single attempt; 2) the Accurate pick-trnsferring protocol, which evaluates the robot's capacity to selectively grasp and transport a specific number of objects from a cluttered environment; and 3) the Pick-transferring-all protocol, which challenges the robot to clear an entire scene by sequentially grasping and transferring all available objects. These protocols are intended to be adopted by the broader robotics research community, providing a standardized method to assess and compare robotic systems' performance in multi-object grasping tasks. We establish baselines for these protocols using standard planning and perception algorithms on a Barrett hand, Robotiq parallel jar gripper, and the Pisa/IIT Softhand-2, which is a soft underactuated robotic hand. We discuss the results in relation to human performance in similar tasks we well.

翻译：本研究提出了一种多目标抓取基准测试方法，用于评估机器人在堆积场景和平面场景下的抓取与操作能力。该基准测试引入了三种机器人多目标抓取评测协议，旨在挑战机器人操作的不同维度。这些协议包括：1) 单次抓取协议，评估机器人在单次尝试中高效抓取多个物体的能力；2) 精确抓取-转移协议，评估机器人在杂乱环境中选择性抓取并转运指定数量物体的能力；3) 全量抓取-转移协议，要求机器人通过连续抓取转移所有可用物体以清空整个场景。这些协议旨在为机器人学研究社区提供标准化评估方法，以衡量和比较不同机器人系统在多目标抓取任务中的性能。我们使用Barrett手、Robotiq平行夹爪以及Pisa/IIT Softhand-2（一种欠驱动软体机械手），基于标准规划与感知算法为这些协议建立了性能基线。我们还将讨论结果与人类在类似任务中的表现进行对比分析。