The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/20536569

Jongeneel, Maarten; Saccon, Alessandro; Job Voorst, van; Menno Lubbers (2022): Impact Aware Manipulation (I.AM.) archive containing suction cup release experiments. Version 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/20536569.v2

Dataset

x choose version: version 1 - 2022-08-30

• Mechanical Engineering

Impact-Aware-Manipulation, Modeling, Object, release-model, Robotics, Suction-cup

CC BY-NC-SA 4.0

• Impact-Aware Robotics Archives Collection
• Impact-Aware Robotics Database

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Maarten Jongeneel , Alessandro Saccon , Job Voorst, van , Menno Lubbers

I.AM. archive as part of the Impact-Aware Robotics Archives Collection. This archive contains all data associated to the paper "Learning Suction Cup Dynamics from Motion Capture: Accurate Prediction of an Object's Vertical Motion during Release", as presented in IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS - Kyoto, Japan, 2022. 

This archive contains recordings of experiments that are executed under the scenario of TOSS. In these recordings, a UR10 robot is used to release and drop a plastic plate with different weights attached on a conveyor belt. The purpose of these experiments is to learn the release dynamics of the suction cup. This modeling framework is used within the H2020 I.AM. project (www.i-am-project.eu) to predict the end pose of a certain box on a conveyor belt, after it is tossed. Within these experiments, the involved contact transition is a release between the robot and the object. All the recordings in the archive were performed at the Innovation Lab of Vanderlande, located within the TU/e campus.
 

• 2022-08-30 first online

• 2022-10-19 published, posted

4TU.ResearchData

md, HDF5

Learning Suction Cup Dynamics from Motion Capture: Accurate Prediction of an Object’s Vertical Motion during Release

• https://ieeexplore.ieee.org/document/9982211

• Impact Aware Manipulation by Dexterous Robot Control and Learning in Dynamic Semi-Structured Logistic Environments (grant code 871899) [more info...] European Commission

Eindhoven University of Technology, Department of Mechanical Engineering, Dynamics and Control Group