2023/06/24 by Shrey Aeron, Edith Llontop, Aeron, Shrey +9
Engineering · Computer Science · #Robot Manipulation and Learning #Modular Robots and Swarm Intelligence #Robotic Path Planning Algorithms
paper · pdf · doi:10.48550/arxiv.2306.14021
Recently, robots have seen rapidly increasing use in homes and warehouses to declutter by collecting objects from a planar surface and placing them into a container. While current techniques grasp objects individually, Multi-Object Grasping (MOG) can improve efficiency by increasing the average number of objects grasped per trip (OpT). However, grasping multiple objects requires the objects to be aligned and in close proximity. In this work, we propose Push-MOG, an algorithm that computes "fork pushing" actions using a parallel-jaw gripper to create graspable object clusters. In physical decluttering experiments, we find that Push-MOG enables multi-object grasps, increasing the average OpT by 34%. Code and videos will be available at https://sites.google.com/berkeley.edu/push-mog.