# Team Delft's Robot Winner of the Amazon Picking Challenge 2016

arXiv: https://arxiv.org/abs/1610.05514 · HTML: https://mlsanity.com/paper/1610.05514/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2016.

## Built on by (most cited first, 10 of 10)

- [Habitat 2.0: Training Home Assistants to Rearrange their Habitat](https://mlsanity.com/paper/2106.14405/) (2021)
- [Sample-Efficient Safety Assurances using Conformal Prediction](https://mlsanity.com/paper/2109.14082/) (2021)
- [SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping](https://mlsanity.com/paper/2103.12311/) (2021)
- [GAgent: An Adaptive Rigid-Soft Gripping Agent with Vision Language Models for Complex Lighting Environments](https://mlsanity.com/paper/2403.10850/) (2024)
- [Model-free Grasping with Multi-Suction Cup Grippers for Robotic Bin Picking](https://mlsanity.com/paper/2307.16488/) (2023)
- [Mechanical Search on Shelves using a Novel "Bluction" Tool](https://mlsanity.com/paper/2201.08968/) (2022)
- [Semi Supervised Deep Quick Instance Detection and Segmentation](https://mlsanity.com/paper/2101.06405/) (2021)
- [OptiGrasp: Optimized Grasp Pose Detection Using RGB Images for Warehouse Picking Robots](https://mlsanity.com/paper/2409.19494/) (2024)
- [Diffusion Suction Grasping with Large-Scale Parcel Dataset](https://mlsanity.com/paper/2502.07238/) (2025)
- [TetraGrip: Sensor-Driven Multi-Suction Reactive Object Manipulation in Cluttered Scenes](https://mlsanity.com/paper/2503.08978/) (2025)

## Abstract (arXiv)

This paper describes Team Delft's robot, which won the Amazon Picking Challenge 2016, including both the Picking and the Stowing competitions. The goal of the challenge is to automate pick and place operations in unstructured environments, specifically the shelves in an Amazon warehouse. Team Delft's robot is based on an industrial robot arm, 3D cameras and a customized gripper. The robot's software uses ROS to integrate off-the-shelf components and modules developed specifically for the competition, implementing Deep Learning and other AI techniques for object recognition and pose estimation, grasp planning and motion planning. This paper describes the main components in the system, and discusses its performance and results at the Amazon Picking Challenge 2016 finals.