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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.20820 |
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| _version_ | 1866908289642528768 |
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| author | Chen, Tianze Frumento, Ricardo Pagnanelli, Giulia Cei, Gianmarco Keth, Villa Gafarov, Shahaddin Gong, Jian Ye, Zihe Baracca, Marco D'Avella, Salvatore Bianchi, Matteo Sun, Yu |
| author_facet | Chen, Tianze Frumento, Ricardo Pagnanelli, Giulia Cei, Gianmarco Keth, Villa Gafarov, Shahaddin Gong, Jian Ye, Zihe Baracca, Marco D'Avella, Salvatore Bianchi, Matteo Sun, Yu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20820 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Benchmarking Multi-Object Grasping Chen, Tianze Frumento, Ricardo Pagnanelli, Giulia Cei, Gianmarco Keth, Villa Gafarov, Shahaddin Gong, Jian Ye, Zihe Baracca, Marco D'Avella, Salvatore Bianchi, Matteo Sun, Yu Robotics 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. |
| title | Benchmarking Multi-Object Grasping |
| topic | Robotics |
| url | https://arxiv.org/abs/2503.20820 |