Fast Object Learning and Dual-arm Coordination for Cluttered Stowing, Picking, and Packing

TL;DR

This paper presents a fast, adaptable robotic system for cluttered object stowing, picking, and packing, utilizing deep perception, transfer learning, and dual-arm coordination, demonstrated successfully in the 2017 Amazon Robotics Challenge.

Contribution

The paper introduces a novel, efficient perception pipeline and dual-arm planning approach that significantly improves speed and adaptability in cluttered object manipulation tasks.

Findings

Achieved second place in ARC picking and stow-and-pick tasks

Developed a transfer learning-based perception system for new items

Demonstrated effective dual-arm coordination reducing execution time

Abstract

Robotic picking from cluttered bins is a demanding task, for which Amazon Robotics holds challenges. The 2017 Amazon Robotics Challenge (ARC) required stowing items into a storage system, picking specific items, and packing them into boxes. In this paper, we describe the entry of team NimbRo Picking. Our deep object perception pipeline can be quickly and efficiently adapted to new items using a custom turntable capture system and transfer learning. It produces high-quality item segments, on which grasp poses are found. A planning component coordinates manipulation actions between two robot arms, minimizing execution time. The system has been demonstrated successfully at ARC, where our team reached second places in both the picking task and the final stow-and-pick task. We also evaluate individual components.