Grasping Unstructured Objects with Full Convolutional Network in Clutter

Abstract

Grasping objects in cluttered environments remains a significant challenge in robotics, particularly when dealing with novel objects that have not been previously encountered. This paper proposes a novel approach to address the problem of robustly learning object grasping in cluttered scenes, focusing on scenarios where the objects are unstructured and randomly placed. We present a unique Deep Q-learning (DQN) framework combined with a full convolutional network suitable for the end-to-end grasping of multiple adhesive objects in a cluttered environment. Our method combines the depth information of objects with reinforcement learning to obtain an adaptive grasping strategy to enable a robot to learn and generalize grasping skills for novel objects in the real world. The experimental results demonstrate that our method significantly improves the grasping performance on novel objects compared to conventional grasping techniques. Our system demonstrates remarkable adaptability and robustness in cluttered scenes, effectively grasping a diverse array of objects that were previously unseen. This research contributes to the advancement of robotics with potential applications, including, but not limited to, redundant manipulators, dual-arm robots, continuum robots, and soft robots.