Self-supervised learning base target picking system for dual-arm robot considering object instance occlusion

Abstract

Recently, robots are introduced to warehouses and factories for automation and are expected to execute dual arm manipulation as human does. We focus on target picking task in the cluttered environment and aim to realize a robot picking system which the robot selects and executes proper grasping motion from single-arm and dual-arm motion. In this paper, we propose a self-supervised learning based target picking system with selective dual-arm grasping. In our system, a robot first learns how to grasp and how to distinguish items with synthesized dataset. The robot then executes and collects grasp trial experiences in the real world and retrains grasping model with the collected trial experiences. Finally, We also propose the learning based target picking system with selective dual-arm grasping and evaluate picking task experiments in the cluttered environment such as warehouse.