Few-Shot Instance Grasping of Novel Objects in Clutter

Abstract

Instance grasping, which aims to grasp a specific object out of clutter, is a fundamental task within robotics. However, allowing a robot to quickly learn to perform instance grasping for new, previously unseen objects remains challenging. In this work, we present an instance grasping meta-learning framework (IGML), a simple yet effective end-to-end approach that not only teaches robots to identify novel objects but also how to grasp them. Given only a few examples to specify the grasping point of the target object, our IGML can quickly learn to recognize the target object and grasp it at the demonstrated grasping point by leveraging prior experience. Experimental results on the test sets show that IGML achieves decent success rates in cluttered environments, significantly surpassing state-of-the-art methods. Then we deployed IGML on a UR5 robot arm to handle pick-and-place scenarios and achieved a precision rate of 93.4% and a recall rate of 87.1%.