A Multi-task Convolutional Neural Network for Autonomous Robotic Grasping in Object Stacking Scenes

TL;DR

This paper introduces a multi-task CNN that enables autonomous robots to identify, plan, and execute grasps in object stacking scenes, effectively handling occlusions and clutter.

Contribution

It presents a novel integrated deep network combining grasp detection and visual relationship reasoning for improved robotic grasping in complex scenes.

Findings

Achieved over 90% success rate in cluttered scenes

Demonstrated effective grasp planning in stacking scenarios

Enhanced robot autonomy in object manipulation

Abstract

Autonomous robotic grasping plays an important role in intelligent robotics. However, how to help the robot grasp specific objects in object stacking scenes is still an open problem, because there are two main challenges for autonomous robots: (1)it is a comprehensive task to know what and how to grasp; (2)it is hard to deal with the situations in which the target is hidden or covered by other objects. In this paper, we propose a multi-task convolutional neural network for autonomous robotic grasping, which can help the robot find the target, make the plan for grasping and finally grasp the target step by step in object stacking scenes. We integrate vision-based robotic grasping detection and visual manipulation relationship reasoning in one single deep network and build the autonomous robotic grasping system. Experimental results demonstrate that with our model, Baxter robot can autonomously grasp the target with a success rate of 90.6%, 71.9% and 59.4% in object cluttered scenes, familiar stacking scenes and complex stacking scenes respectively.