Fast Object Learning and Dual-arm Coordination for Cluttered Stowing, Picking, and Packing
This addresses the problem of efficient robotic manipulation in cluttered environments for logistics and automation, representing a competitive but incremental improvement.
The paper tackled robotic picking from cluttered bins by developing a system with fast object learning and dual-arm coordination, achieving second place in two tasks at the Amazon Robotics Challenge.
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.