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

arXiv: https://arxiv.org/abs/1810.02977 · HTML: https://mlsanity.com/paper/1810.02977/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2018.

## Built on by (most cited first, 12 of 12)

- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping](https://mlsanity.com/paper/2103.12311/) (2021)
- [T6D-Direct: Transformers for Multi-Object 6D Pose Direct Regression](https://mlsanity.com/paper/2109.10948/) (2021)
- [Tac2Pose: Tactile Object Pose Estimation from the First Touch](https://mlsanity.com/paper/2204.11701/) (2022)
- [Synthetic-to-Real Domain Adaptation using Contrastive Unpaired Translation](https://mlsanity.com/paper/2203.09454/) (2022)
- [SynPick: A Dataset for Dynamic Bin Picking Scene Understanding](https://mlsanity.com/paper/2107.04852/) (2021)
- [YOLOPose V2: Understanding and Improving Transformer-based 6D Pose Estimation](https://mlsanity.com/paper/2307.11550/) (2023)
- [Learning Embeddings with Centroid Triplet Loss for Object Identification in Robotic Grasping](https://mlsanity.com/paper/2404.06277/) (2024)
- [Semi Supervised Deep Quick Instance Detection and Segmentation](https://mlsanity.com/paper/2101.06405/) (2021)
- [Predicting Physical Object Properties from Video](https://mlsanity.com/paper/2206.00930/) (2022)
- [Semantic Interaction in Augmented Reality Environments for Microsoft HoloLens](https://mlsanity.com/paper/2112.05846/) (2021)
- [A Review of Scene Representations for Robot Manipulators](https://mlsanity.com/paper/2301.11275/) (2022)

## Abstract (arXiv)

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.