提出多物体抓取基准，评估机器人在杂乱场景下的抓取与搬运能力。
Benchmarking Multi-Object Grasping
- 设计三种抓取协议，分别测试单次抓取、精准搬运和全场景清空能力。
- 在堆叠与表面场景中，三款机械手均未达到人类水平的抓取成功率。
- 为机器人研究提供标准化评估方法，适合机器人抓取算法开发者使用。
本文介绍了一个多物体抓取基准，用于评估机器人在堆叠和表面场景中的抓取与操作能力。该基准包含三种机器人多物体抓取评测协议，分别挑战不同方面的机器人操作能力：1）仅一次抓取协议，评估机器人单次尝试中高效抓取多个物体的能力；2）精准抓取搬运协议，评估机器人从杂乱环境中选择性抓取并运输特定数量物体的能力；3）全部抓取搬运协议，要求机器人通过依次抓取并转移所有可用物体来清理整个场景。这些协议旨在被更广泛的机器人研究社区采纳，提供一种标准化方法以评估和比较机器人系统在多物体抓取任务中的表现。我们使用标准规划与感知算法，在Barrett手、Robotiq平行夹持器以及Pisa/IIT Softhand-2（一款软体欠驱动机械手）上建立了基线。结果分析与人类在类似任务中的表现进行了对比。
原文摘要 · Abstract (English)
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.
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