Multi-Object Grasping—Experience Forest for Robotic Finger Movement Strategies

Abstract

This paper introduces a novel Experience Forest algorithm designed for multi-object grasping (MOG). Different from single-object grasping, for MOG, the hand poses of a few steps before the end of grasping play important roles in the success of MOG. But similar to single-object grasping, the hand poses that are far from the end grasping pose are not as relevant. Therefore, the proposed approach invented the Experience Forest structure to organize the finger movement sequences collected in naive MOG approaches with a set of trees instead of a single tree. The algorithm propagates success or failure results in the trials from end-pose nodes only to the nodes representing several preceding hand poses. When using the trees to generate a grasping sequence, the algorithm generates a finger-movement policy that follows a MOG synergy at the beginning and then transits to a tree in the Experience Forest and then employs a breadth-first search to achieve a more reliable solution. Tested on various objects using a UR5e robotic arm and Barrett hand in both simulated and real environments, the strategy significantly boosts efficiency in object transfer tasks by up to 60%, marking a 10% improvement over our previous methods.