Published on Sun Jul 23 2017

Team Applied Robotics: A closer look at our robotic picking system

Wim Abbeloos, Fabian Gouwens, Simon Jansen, Berend Küpers, Maurice Ramaker, Toon Goedemé

This paper describes the vision based robotic picking system that was developed by our team, Team Applied Robotics, for the Amazon Picking Challenge 2016. This competition challenged teams to develop a robotic system that is able to pick a large variety of products from a shelve or a tote.

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Abstract

This paper describes the vision based robotic picking system that was developed by our team, Team Applied Robotics, for the Amazon Picking Challenge 2016. This competition challenged teams to develop a robotic system that is able to pick a large variety of products from a shelve or a tote. We discuss the design considerations and our strategy, the high resolution 3D vision system, the use of a combination of texture and shape-based object detection algorithms, the robot path planning and object manipulators that were developed.

Sat Sep 17 2016
Artificial Intelligence
The ACRV Picking Benchmark (APB): A Robotic Shelf Picking Benchmark to Foster Reproducible Research
The ACRV Picking Benchmark (APB) is designed to be reproducible. It consists of a set of 42 common objects, a widely available shelf, and exact guidelines for object arrangement.
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Thu Mar 05 2020
Computer Vision
Team O2AS at the World Robot Summit 2018: An Approach to Robotic Kitting and Assembly Tasks using General Purpose Grippers and Tools
We propose a versatile robotic system for kitting and assembly tasks. Instead of specialized end effectors, it uses its two-finger grippers to grasp and hold tools. A third gripper is used as a precision picking and centering tool.
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Tue Oct 03 2017
Computer Vision
Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching
A robotic pick-and-place system that is capable of grasping and recognizing both known and novel objects in cluttered environments. The key new feature of the system is that it handles a wide range of object categories without needing any task-specific training data.
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Thu Sep 29 2016
Machine Learning
Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge
MIT-Princeton Team system took 3rd- and 4th- place in the stowing andpicking tasks, respectively at APC 2016.
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Thu Sep 26 2019
Artificial Intelligence
RLBench: The Robot Learning Benchmark & Learning Environment
RLBench features 100 unique, hand-designed tasks ranging in difficulty. Each task comes with an infinite supply of demos through the use of motion planners. The benchmark aims to accelerate progress in a number of vision-guided manipulation research.
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Mon Jul 29 2013
Artificial Intelligence
Integration of 3D Object Recognition and Planning for Robotic Manipulation: A Preliminary Report
We investigate different approaches to integrating object recognition and planning in a tabletop manipulation domain. Results of our preliminary experiments show that close integration of perception and planning improves the quality of plans, as well as the computation times of feasible plans.
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