Benchmarking Multi-Object Grasping

--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. The authors are from the Robot Perception and Action Lab (RP AL) of Computer Science and Engineering Department, University of South Florida, Tampa, FL 33620, USA. The authors are with the Research Center "E. The author is with is with Rutgers University, New Brunswick, NJ 08901, USA. Related work was finished when Zihe Y e was a Master's student in the RP AL lab at USF. The author is with the Department of Excellence in Robotics & AI, Mechanical Intelligence Institute, Scuola Superiore Sant'Anna, Pisa, Italy.