VERtical Innovation in the Domain of Robotics Enabled by Artificial intelligence Methods

Informazioni relative al progetto

VeriDream

ID dell’accordo di sovvenzione: 951992

DOI

10.3030/951992

Progetto chiuso

Data della firma CE 26 Giugno 2020

Data di avvio 1 Ottobre 2020

Data di completamento 31 Marzo 2023

Finanziato da

• EXCELLENT SCIENCE - Future and Emerging Technologies (FET)

Costo totale

€ 1 988 670,00

Contributo UE

€ 1 988 670,00

Coordinato da

DEUTSCHES ZENTRUM FUR LUFT - UND RAUMFAHRT EV
Germany

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Documents, reports (4)

Guidelines for using DREAM methods

A report on the scope and limitation of the DREAM methods along with pragmatic guidelines for deploying and adapting them to different types of use cases

Scientific paper or report on generalisation and robustification through exploration

A collection of scientific papers on individual methods or an overview article over all methods The deliverable is relatively late in the project to account for reviewing periods

Broad Exploitation Strategy Report

Report describing the strategy for exploiting the innovation potential of VERIDREAM outside the boundaries of the projectAfter Synesis participation in the project has ended the content of this deliverable has been refined to include Interviews with academic leaders who have made a startup in AI and their lessons learned from that eg Pieter Abbeel George Konidaris A strategy for patenting AI which is not possible in the EU as such when applied to robotics which facilitates patenting AI A concept for IP management wrt to academic opensource software as background in EU project and its compatibility with DESCA A metaanalysis of the innovation loop ie the main lessons learned from it

Scientific paper or report on generalisation and robustification through representation

A collection of scientific papers on individual methods or an overview article over all methods The deliverable is relatively late in the project to account for reviewing periods

Websites, patent fillings, videos etc. (3)

Coordination with the AI4EU platform

Document describing the interaction with the AI4EU platform in terms of dissemination as well as the strategy for publishing data and software on AI4EU

Scientific workshop

A scientific workshop to be organised at IROS ICRA RSS or ICDLEpiRob

Project Website Publication

Document describing the interaction with the AI4EU platform in terms of dissemination as well as the strategy for publishing data and software on AI4EU

Demonstrators, pilots, prototypes (1)

Demonstration of GoodAI Use Cases

A demonstration of the DREAM methods within the Space Engineers video game.

Pubblicazioni

Peer reviewed articles (6)

Stable-Baselines3: Reliable Reinforcement Learning Implementations

Autori: Raffin, Antonin; Hill, Ashley; Gleave, Adam; Kanervisto, Anssi; Ernestus, Maximilian; Dormann, Noah
Pubblicato in: Journal of Machine Learning Research (JMLR), Numero 6, 2021, ISSN 1533-7928
Editore: Microtome Publishing

Few-shot Quality-Diversity Optimization

Autori: Achkan, Salehi; Salehi, Achkan; Coninx, Alexandre; Doncieux, Stephane
Pubblicato in: https://hal.archives-ouvertes.fr/hal-03569179, Numero 3, 2022, ISSN 2377-3766
Editore: Institute of Electrical and Electronics Engineers Inc.
DOI: 10.1109/lra.2022.3148438

Smooth Exploration for Robotic Reinforcement Learning

Autori: Raffin, Antonin; Kober, Jens; Stulp, Freek
Pubblicato in: Conference on Robot Learning, Numero 3, 2022, Pagina/e 1634-1644, ISSN 2640-3498
Editore: PMLR
DOI: 10.48550/arxiv.2005.05719

Innovation Paths for Machine Learning in Robotics

Autori: Stulp, Freek; Spranger, Michael; Listmann, Kim; Doncieux, Stéphane
Pubblicato in: IEEE Robotics & Automation Magazine, Numero 29(4), 2022, Pagina/e 141--144, ISSN 1070-9932
Editore: Institute of Electrical and Electronics Engineers
DOI: 10.1109/mra.2022.3213205

Fault-Tolerant Six-DoF Pose Estimation for Tendon-Driven Continuum Mechanisms

Autori: Antonin Raffin, Bastian Deutschmann, Freek Stulp
Pubblicato in: Frontiers in Robotics and AI, Numero 8, 2021, ISSN 2296-9144
Editore: Frontiers Media
DOI: 10.3389/frobt.2021.619238

Exploratory State Representation Learning

Autori: Astrid Merckling; Nicolas Perrin-Gilbert; Alex Coninx; Stéphane Doncieux
Pubblicato in: Frontiers in Robotics and AI, Vol 9 (2022), Numero 8, 2022, ISSN 2296-9144
Editore: Frontiers Media SA
DOI: 10.48550/arxiv.2109.13596

Other (4)

Learning to Exploit Elastic Actuators for Quadruped Locomotion

Autori: Antonin Raffin, Daniel Seidel, Jens Kober, Alin Albu-Schäffer, João Silvério, Freek Stulp
Pubblicato in: 2022
Editore: arxiv
DOI: 10.48550/arxiv.2209.07171

Apprendre aux robots à faire face à l'imprévu

Autori: Stephane Doncieux
Pubblicato in: Industries et Technologies [Cahier Technique], Numero 1045, 2021
Editore: Infopro

Transferring AI research results into an industrial product

Autori: "Steffen R\""uhl, Moritz Tenorth, Achkan Salehi, Stéphane Doncieux"
Pubblicato in: White paper, 2023
Editore: Magazino

Adaptive Asynchronous Control Using Meta-learned Neural Ordinary Differential Equations

Autori: Achkan Salehi, Steffen Rühl, Stéphane Doncieux
Pubblicato in: 2022
Editore: arxiv

Conference proceedings (4)

Reinforcement Learning Guided by Shared Control Templates

Autori: Abhishek Padalkar, Gabriel Quere, Franz Steinmetz, Antonin Raffin, Matthias Nieuwenhuisen, Joao Silverio, Freek Stulp
Pubblicato in: Proceedings of the IEEE-ICRA conference, 2023
Editore: IEEE

BR-NS - an archive-less approach to novelty search

Autori: Achkan Salehi, Alexandre Coninx, Stephane Doncieux
Pubblicato in: Proceedings of the Genetic and Evolutionary Computation Conference, 2021, Pagina/e 172-179, ISBN 9781450383509
Editore: ACM
DOI: 10.1145/3449639.3459303

Closing the Gap: Combining Task Specification and Reinforcement Learning for Compliant Vegetable Cutting

Autori: Padalkar, Abhishek; Nieuwenhuisen, Matthias; Schulz, Dirk; Stulp, Freek
Pubblicato in: Informatics in Control, Automation and Robotics ISBN: 9783030924416, Numero 3, 2022, ISBN 9783030924416
Editore: Springer
DOI: 10.1007/978-3-030-92442-3_11

Smooth Exploration for Robotic Reinforcement Learning

Autori: Antonin Raffin and Jens Kober and Freek Stulp
Pubblicato in: Conference on Robot Learning, 2021
Editore: openreview.net