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

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.

arXiv commentIn: Proceedings of the International Conference on Robotics and Automation (ICRA) 2018

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• Supervised Autonomous Locomotion and Manipulation for Disaster Response with a Centaur-like Robot
• Tac2Pose: Tactile Object Pose Estimation from the First Touch
• Active Visuo-Tactile Interactive Robotic Perception for Accurate Object Pose Estimation in Dense Clutter
• Synthetic-to-Real Domain Adaptation using Contrastive Unpaired Translation
• Tactile Object Pose Estimation from the First Touch with Geometric Contact Rendering
• Tight Robot Packing in the Real World: A Complete Manipulation Pipeline with Robust Primitives
• Robust 6D Object Pose Estimation in Cluttered Scenes using Semantic Segmentation and Pose Regression Networks
• kPAM 2.0: Feedback Control for Category-Level Robotic Manipulation
• Joint Learning of Instance and Semantic Segmentation for Robotic Pick-and-Place with Heavy Occlusions in Clutter
• Semi Supervised Deep Quick Instance Detection and Segmentation