- home
- Advanced Search
Filters
Clear All- Rural Digital Europe
- Publications
- Research data
- Other research products
- Preprint
- European Commission
- EC|H2020
- EC|H2020|RIA
- INRIA a CCSD electronic archive ser...
- Rural Digital Europe
- Publications
- Research data
- Other research products
- Preprint
- European Commission
- EC|H2020
- EC|H2020|RIA
- INRIA a CCSD electronic archive ser...
Loading
description Publicationkeyboard_double_arrow_right Article , Preprint , Other literature type 2021 Italy, Italy, Italy, Italy, Finland, Belgium, Italy, Italy, Netherlands, France, France, Italy, Italy, France, Switzerland, FrancePublisher:Cold Spring Harbor Laboratory Funded by:EC | SHOWCASEEC| SHOWCASERocchini; Duccio; Thouverai; Elisa; Marcantonio; Matteo; Iannacito; Martina; Da Re; Daniele; Torresani; Michele; Bacaro; Giovanni; Bazzichetto; Manuele; Bernardi; Alessandra; Foody; Giles M.; Furrer; Reinhard; Kleijn; David; Larsen; Stefano; Lenoir; Jonathan; Malavasi; Marco; Marchetto; Elisa; Messori; Filippo; Montaghi; Alessandro; Moudr'y; V'itvezslav; Naimi; Babak; Ricotta; Carlo; Rossini; Micol; Santi; Francesco; Santos; Maria J.; Schaepman; Michael E.; Schneider; Fabian D.; Schuh; Leila; Silvestri; Sonia; ?'imov'a; Petra; Skidmore; Andrew K.; Tattoni; Clara; Tordoni; Enrico; Vicario; Saverio; Zannini; Piero; Wegmann; Martin;doi: 10.1101/2021.02.09.430391 , 10.5281/zenodo.7081753 , 10.5167/uzh-205477 , 10.5281/zenodo.7081752 , 10.1111/2041-210x.13583
handle: 10449/67542 , 11368/2980231 , 2078.1/254199 , 11572/304627
pmid: 34262682
pmc: PMC8252722
doi: 10.1101/2021.02.09.430391 , 10.5281/zenodo.7081753 , 10.5167/uzh-205477 , 10.5281/zenodo.7081752 , 10.1111/2041-210x.13583
handle: 10449/67542 , 11368/2980231 , 2078.1/254199 , 11572/304627
pmid: 34262682
pmc: PMC8252722
AbstractEcosystem heterogeneity has been widely recognized as a key ecological feature, influencing several ecological functions, since it is strictly related to several ecological functions like diversity patterns and change, metapopulation dynamics, population connectivity, or gene flow.In this paper, we present a new R package - rasterdiv - to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns.The rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open source algorithms.
Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2021Full-Text: http://europepmc.org/articles/PMC8252722Data sources: PubMed CentralMethods in Ecology and EvolutionArticle . 2021Full-Text: https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2021/isi/skidmore_ras.pdfData sources: NARCISNARCIS; Research@WUROther literature type . Article . 2021License: CC BYFull-Text: https://edepot.wur.nl/547729HELDA - Digital Repository of the University of HelsinkiArticle . 2021 . Peer-reviewedData sources: HELDA - Digital Repository of the University of HelsinkiArchivio della ricerca- Università di Roma La SapienzaArticle . 2021Data sources: Archivio della ricerca- Università di Roma La Sapienzahttps://doi.org/10.1101/2021.0...Preprint . 2021add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1101/2021.02.09.430391&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 12 citations 12 popularity Top 10% influence Average impulse Top 10% Powered by BIP!visibility 19visibility views 19 download downloads 46 Powered bymore_vert Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2021Full-Text: http://europepmc.org/articles/PMC8252722Data sources: PubMed CentralMethods in Ecology and EvolutionArticle . 2021Full-Text: https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2021/isi/skidmore_ras.pdfData sources: NARCISNARCIS; Research@WUROther literature type . Article . 2021License: CC BYFull-Text: https://edepot.wur.nl/547729HELDA - Digital Repository of the University of HelsinkiArticle . 2021 . Peer-reviewedData sources: HELDA - Digital Repository of the University of HelsinkiArchivio della ricerca- Università di Roma La SapienzaArticle . 2021Data sources: Archivio della ricerca- Università di Roma La Sapienzahttps://doi.org/10.1101/2021.0...Preprint . 2021add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1101/2021.02.09.430391&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2020 Italy, France, Spain, FrancePublisher:Elsevier BV Funded by:EC | DREAMEC| DREAMLesort, Timothée; Lomonaco, Vincenzo; Stoian, Andrei; Maltoni, Davide; Filliat, David; Díaz-Rodríguez, Natalia;Abstract Continual learning (CL) is a particular machine learning paradigm where the data distribution and learning objective change through time, or where all the training data and objective criteria are never available at once. The evolution of the learning process is modeled by a sequence of learning experiences where the goal is to be able to learn new skills all along the sequence without forgetting what has been previously learned. CL can be seen as an online learning where knowledge fusion needs to take place in order to learn from streams of data presented sequentially in time. Continual learning also aims at the same time at optimizing the memory, the computation power and the speed during the learning process. An important challenge for machine learning is not necessarily finding solutions that work in the real world but rather finding stable algorithms that can learn in real world. Hence, the ideal approach would be tackling the real world in a embodied platform: an autonomous agent. Continual learning would then be effective in an autonomous agent or robot, which would learn autonomously through time about the external world, and incrementally develop a set of complex skills and knowledge.Robotic agents have to learn to adapt and interact with their environment using a continuous stream of observations. Some recent approaches aim at tackling continual learning for robotics, but most recent papers on continual learning only experiment approaches in simulation or with static datasets. Unfortunately, the evaluation of those algorithms does not provide insights on whether their solutions may help continual learning in the context of robotics. This paper aims at reviewing the existing state of the art of continual learning, summarizing existing benchmarks and metrics, and proposing a framework for presenting and evaluating both robotics and non robotics approaches in a way that makes transfer between both fields easier. We put light on continual learning in the context of robotics to create connections between fields and normalize approaches.
Information Fusion arrow_drop_down Repositorio Institucional Universidad de GranadaArticle . 2020Data sources: Repositorio Institucional Universidad de GranadaINRIA a CCSD electronic archive server; Mémoires en Sciences de l'Information et de la CommunicationArticle . 2019License: CC BY NCFull-Text: https://hal.science/hal-02381343/documenthttps://doi.org/10.48550/arxiv...Article . 2019License: arXiv Non-Exclusive DistributionData sources: DataciteArchivio della Ricerca - Università di PisaArticle . 2020Data sources: Archivio della Ricerca - Università di Pisaadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.inffus.2019.12.004&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 188 citations 188 popularity Top 0.1% influence Top 1% impulse Top 0.1% Powered by BIP!more_vert Information Fusion arrow_drop_down Repositorio Institucional Universidad de GranadaArticle . 2020Data sources: Repositorio Institucional Universidad de GranadaINRIA a CCSD electronic archive server; Mémoires en Sciences de l'Information et de la CommunicationArticle . 2019License: CC BY NCFull-Text: https://hal.science/hal-02381343/documenthttps://doi.org/10.48550/arxiv...Article . 2019License: arXiv Non-Exclusive DistributionData sources: DataciteArchivio della Ricerca - Università di PisaArticle . 2020Data sources: Archivio della Ricerca - Università di Pisaadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.inffus.2019.12.004&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2019 Netherlands, Netherlands, Netherlands, FrancePublisher:Springer Science and Business Media LLC Funded by:EC | ReMIXEC| ReMIXAuthors: Noémie Gaudio; Abraham J. Escobar-Gutiérrez; Pierre Casadebaig; Jochem B. Evers; +17 AuthorsNoémie Gaudio; Abraham J. Escobar-Gutiérrez; Pierre Casadebaig; Jochem B. Evers; Frédéric Gérard; Gaëtan Louarn; Nathalie Colbach; Sebastian Munz; Marie Launay; Hélène Marrou; Romain Barillot; Philippe Hinsinger; Jacques Eric Bergez; Didier Combes; Jean-Louis Durand; Ela Frak; Loïc Pagès; Christophe Pradal; Sébastien Saint-Jean; Wopke van der Werf; Eric Justes;Growing mixtures of annual arable crop species or genotypes is a promising way to improve crop production without increasing agricultural inputs. To design optimal crop mixtures, choices of species, genotypes, sowing proportion, plant arrangement, and sowing date need to be made but field experiments alone are not sufficient to explore such a large range of factors. Crop modeling allows to study, understand and ultimately design cropping systems and is an established method for sole crops. Recently, modeling started to be applied to annual crop mixtures as well. Here, we review to what extent crop simulation models and individual-based models are suitable to capture and predict the specificities of annual crop mixtures. We argued that: 1) The crop mixture spatio-temporal heterogeneity (influencing the occurrence of ecological processes) determines the choice of the modeling approach (plant or crop centered). 2) Only few crop models (adapted from sole crop models) and individual-based models currently exist to simulate annual crop mixtures. 3) Crop models are mainly used to address issues related to crop mixtures management and to the integration of crop mixtures into larger scales such as the rotation, whereas individual-based models are mainly used to identify plant traits involved in crop mixture performance and to quantify the relative contribution of the different ecological processes (niche complementarity, facilitation, competition, plasticity) to crop mixture functioning. This review highlights that modeling of annual crop mixtures is in its infancy and gives to model users some important keys to choose the model based on the questions they want to answer, with awareness of the strengths and weaknesses of each of the modeling approaches. Comment: 42 pages, 5 figures
Agritrop arrow_drop_down Research@WUR; Agronomy for Sustainable DevelopmentOther literature type . Article . 2019 . Peer-reviewedLicense: Springer TDMHAL - UPEC / UPEM; HAL-Pasteur; HAL-Inserm; Hal-DiderotArticle . 2019Full-Text: https://hal.inria.fr/hal-02228974/documentadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/s13593-019-0562-6&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 74 citations 74 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!more_vert Agritrop arrow_drop_down Research@WUR; Agronomy for Sustainable DevelopmentOther literature type . Article . 2019 . Peer-reviewedLicense: Springer TDMHAL - UPEC / UPEM; HAL-Pasteur; HAL-Inserm; Hal-DiderotArticle . 2019Full-Text: https://hal.inria.fr/hal-02228974/documentadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/s13593-019-0562-6&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Preprint , Article 2018 Spain, FrancePublisher:Elsevier BV Funded by:EC | DREAMEC| DREAMAuthors: Timothée Lesort; Natalia Díaz-Rodríguez; Jean-Francois Goudou; David Filliat;Timothée Lesort; Natalia Díaz-Rodríguez; Jean-Francois Goudou; David Filliat;International audience; Representation learning algorithms are designed to learn abstract features that characterize data. State representation learning (SRL) focuses on a particular kind of representation learning where learned features are in low dimension, evolve through time, and are influenced by actions of an agent. The representation is learned to capture the variation in the environment generated by the agent's actions; this kind of representation is particularly suitable for robotics and control scenarios. In particular, the low dimension characteristic of the representation helps to overcome the curse of dimensionality, provides easier interpretation and utilization by humans and can help improve performance and speed in policy learning algorithms such as reinforcement learning. This survey aims at covering the state-of-the-art on state representation learning in the most recent years. It reviews different SRL methods that involve interaction with the environment , their implementations and their applications in robotics control tasks (simulated or real). In particular, it highlights how generic learning objectives are differently exploited in the reviewed algorithms. Finally, it discusses evaluation methods to assess the representation learned and summarizes current and future lines of research.
arXiv.org e-Print Ar... arrow_drop_down Repositorio Institucional Universidad de GranadaArticle . 2018Data sources: Repositorio Institucional Universidad de Granadahttps://doi.org/10.48550/arxiv...Article . 2018License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.neunet.2018.07.006&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 134 citations 134 popularity Top 1% influence Top 1% impulse Top 1% Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down Repositorio Institucional Universidad de GranadaArticle . 2018Data sources: Repositorio Institucional Universidad de Granadahttps://doi.org/10.48550/arxiv...Article . 2018License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.neunet.2018.07.006&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Preprint , Article 2018 FrancePublisher:IEEE Funded by:EC | An.Dy, EC | ResiBotsEC| An.Dy ,EC| ResiBotsAuthors: Chatzilygeroudis, Konstantinos; Mouret, Jean-Baptiste;Chatzilygeroudis, Konstantinos; Mouret, Jean-Baptiste;The most data-efficient algorithms for reinforcement learning in robotics are model-based policy search algorithms, which alternate between learning a dynamical model of the robot and optimizing a policy to maximize the expected return given the model and its uncertainties. Among the few proposed approaches, the recently introduced Black-DROPS algorithm exploits a black-box optimization algorithm to achieve both high data-efficiency and good computation times when several cores are used; nevertheless, like all model-based policy search approaches, Black-DROPS does not scale to high dimensional state/action spaces. In this paper, we introduce a new model learning procedure in Black-DROPS that leverages parameterized black-box priors to (1) scale up to high-dimensional systems, and (2) be robust to large inaccuracies of the prior information. We demonstrate the effectiveness of our approach with the "pendubot" swing-up task in simulation and with a physical hexapod robot (48D state space, 18D action space) that has to walk forward as fast as possible. The results show that our new algorithm is more data-efficient than previous model-based policy search algorithms (with and without priors) and that it can allow a physical 6-legged robot to learn new gaits in only 16 to 30 seconds of interaction time. Accepted at ICRA 2018; 8 pages, 4 figures, 2 algorithms, 1 table; Video at https://youtu.be/HFkZkhGGzTo ; Spotlight ICRA presentation at https://youtu.be/_MZYDhfWeLc
http://arxiv.org/pdf... arrow_drop_down Hal-DiderotConference object . 2018Full-Text: https://hal.inria.fr/hal-01768285/documentData sources: Hal-Diderothttps://doi.org/10.1109/icra.2...Other literature type . Conference object . 2018 . Peer-reviewedhttps://doi.org/10.48550/arxiv...Article . 2017License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/icra.2018.8461083&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 16 citations 16 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!more_vert http://arxiv.org/pdf... arrow_drop_down Hal-DiderotConference object . 2018Full-Text: https://hal.inria.fr/hal-01768285/documentData sources: Hal-Diderothttps://doi.org/10.1109/icra.2...Other literature type . Conference object . 2018 . Peer-reviewedhttps://doi.org/10.48550/arxiv...Article . 2017License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/icra.2018.8461083&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Other literature type , Article , Preprint 2018Embargo end date: 01 Jan 2018 France, Germany, SwitzerlandPublisher:arXiv Funded by:EC | An.Dy, EC | RISE, CHIST-ERA | Heap +2 projectsEC| An.Dy ,EC| RISE ,CHIST-ERA| Heap ,EC| ResiBots ,EC| MEMMOAuthors: Konstantinos Chatzilygeroudis; Vassilis Vassiliades; Freek Stulp; Sylvain Calinon; +1 AuthorsKonstantinos Chatzilygeroudis; Vassilis Vassiliades; Freek Stulp; Sylvain Calinon; Jean-Baptiste Mouret;Most policy search algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot. This survey article focuses on the extreme other end of the spectrum: how can a robot adapt with only a handful of trials (a dozen) and a few minutes? By analogy with the word "big-data", we refer to this challenge as "micro-data reinforcement learning". We show that a first strategy is to leverage prior knowledge on the policy structure (e.g., dynamic movement primitives), on the policy parameters (e.g., demonstrations), or on the dynamics (e.g., simulators). A second strategy is to create data-driven surrogate models of the expected reward (e.g., Bayesian optimization) or the dynamical model (e.g., model-based policy search), so that the policy optimizer queries the model instead of the real system. Overall, all successful micro-data algorithms combine these two strategies by varying the kind of model and prior knowledge. The current scientific challenges essentially revolve around scaling up to complex robots (e.g., humanoids), designing generic priors, and optimizing the computing time. Comment: 21 pages, 3 figures, 4 algorithms, accepted at IEEE Transactions on Robotics
IEEE Transactions on... arrow_drop_down IEEE Transactions on Robotics; DLR publication serverOther literature type . Article . 2019License: CC BYInfoscience - EPFL scientific publicationsOther literature typeData sources: Infoscience - EPFL scientific publicationsHAL - UPEC / UPEM; HAL-Pasteur; HAL-Inserm; Hal-DiderotArticle . 2020Full-Text: https://hal.inria.fr/hal-02393432/documentadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.48550/arxiv.1807.02303&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!visibility 43visibility views 43 download downloads 43 Powered bymore_vert IEEE Transactions on... arrow_drop_down IEEE Transactions on Robotics; DLR publication serverOther literature type . Article . 2019License: CC BYInfoscience - EPFL scientific publicationsOther literature typeData sources: Infoscience - EPFL scientific publicationsHAL - UPEC / UPEM; HAL-Pasteur; HAL-Inserm; Hal-DiderotArticle . 2020Full-Text: https://hal.inria.fr/hal-02393432/documentadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.48550/arxiv.1807.02303&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Article , Preprint 2017 FrancePublisher:IEEE Funded by:EC | RoMaNSEC| RoMaNSAuthors: Ghalamzan, Amir, M; Abi-Farraj, Firas; Robuffo Giordano, Paolo; Stolkin, Rustam;Ghalamzan, Amir, M; Abi-Farraj, Firas; Robuffo Giordano, Paolo; Stolkin, Rustam;This paper addresses the problem of mixed initiative, shared control for master-slave grasping and manipulation. We propose a novel system, in which an autonomous agent assists a human in teleoperating a remote slave arm/gripper, using a haptic master device. Our system is designed to exploit the human operator's expertise in selecting stable grasps (still an open research topic in autonomous robotics). Meanwhile, a-priori knowledge of: i) the slave robot kinematics, and ii) the desired post-grasp manipulative trajectory, are fed to an autonomous agent which transmits force cues to the human, to encourage maximally manipulable grasp pose selections. Specifically, the autonomous agent provides force cues to the human, during the reach-to-grasp phase, which encourage the human to select grasp poses which maximise manipulation capability during the post-grasp object manipulation phase. We introduce a task-relevant velocity manipulability cost function (TOV), which is used to identify the maximum kinematic capability of a manipulator during post-grasp motions, and feed this back as force cues to the human during the pre-grasp phase. We show that grasps which minimise TOV result in significantly reduced control effort of the manipulator, compared to other feasible grasps. We demonstrate the effectiveness of our approach by experiments with both real and simulated robots. Comment: To be appeared in IEEE/RAS IROS 2017
arXiv.org e-Print Ar... arrow_drop_down HAL-Rennes 1; Hal-DiderotConference object . 2017Full-Text: https://hal.inria.fr/hal-01572347/documenthttps://doi.org/10.48550/arxiv...Article . 2017License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/iros.2017.8206178&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 19 citations 19 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down HAL-Rennes 1; Hal-DiderotConference object . 2017Full-Text: https://hal.inria.fr/hal-01572347/documenthttps://doi.org/10.48550/arxiv...Article . 2017License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/iros.2017.8206178&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Article , Preprint , Other literature type 2017 FrancePublisher:IEEE Funded by:EC | An.Dy, EC | ResiBotsEC| An.Dy ,EC| ResiBotsChatzilygeroudis, Konstantinos; Rama, Roberto; Kaushik, Rituraj; Goepp, Dorian; Vassiliades, Vassilis; Mouret, Jean-Baptiste;The most data-efficient algorithms for reinforcement learning (RL) in robotics are based on uncertain dynamical models: after each episode, they first learn a dynamical model of the robot, then they use an optimization algorithm to find a policy that maximizes the expected return given the model and its uncertainties. It is often believed that this optimization can be tractable only if analytical, gradient-based algorithms are used; however, these algorithms require using specific families of reward functions and policies, which greatly limits the flexibility of the overall approach. In this paper, we introduce a novel model-based RL algorithm, called Black-DROPS (Black-box Data-efficient RObot Policy Search) that: (1) does not impose any constraint on the reward function or the policy (they are treated as black-boxes), (2) is as data-efficient as the state-of-the-art algorithm for data-efficient RL in robotics, and (3) is as fast (or faster) than analytical approaches when several cores are available. The key idea is to replace the gradient-based optimization algorithm with a parallel, black-box algorithm that takes into account the model uncertainties. We demonstrate the performance of our new algorithm on two standard control benchmark problems (in simulation) and a low-cost robotic manipulator (with a real robot). Comment: Accepted at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2017; Code at http://github.com/resibots/blackdrops; Video at http://youtu.be/kTEyYiIFGPM
http://arxiv.org/pdf... arrow_drop_down Hal-DiderotConference object . 2017Full-Text: https://hal.inria.fr/hal-01576683/documentData sources: Hal-Diderothttps://doi.org/10.1109/iros.2...Conference object . 2017 . Peer-reviewedhttps://doi.org/10.48550/arxiv...Article . 2017License: arXiv Non-Exclusive DistributionData sources: Datacitehttps://doi.org/10.1109/IROS.2...Other literature type . Conference object . 2017Data sources: European Union Open Data Portaladd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/iros.2017.8202137&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 48 citations 48 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!more_vert http://arxiv.org/pdf... arrow_drop_down Hal-DiderotConference object . 2017Full-Text: https://hal.inria.fr/hal-01576683/documentData sources: Hal-Diderothttps://doi.org/10.1109/iros.2...Conference object . 2017 . Peer-reviewedhttps://doi.org/10.48550/arxiv...Article . 2017License: arXiv Non-Exclusive DistributionData sources: Datacitehttps://doi.org/10.1109/IROS.2...Other literature type . Conference object . 2017Data sources: European Union Open Data Portaladd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/iros.2017.8202137&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2017 France, Spain, France, FrancePublisher:Institute of Electrical and Electronics Engineers (IEEE) Funded by:EC | ARMOUR, EC | F-InteropEC| ARMOUR ,EC| F-InteropAdelantado, Ferran; Vilajosana, Xavier; Tuset-Peiro, Pere; Martinez, Borja; Melia, Joan; Watteyne, Thomas;handle: 10609/93072
International audience; Low-Power Wide Area Networking (LPWAN) technologyoffers long-range communication, which enables new typesof services. Several solutions exist; LoRaWAN is arguablethe most adopted. It promises ubiquitous connectivity inoutdoor IoT applications, while keeping network structures,and management, simple. This technology has received a lotof attention in recent months from network operators andsolution providers. Yet, the technology has limitations thatneed to be clearly understood to avoid inflated expectationsand disillusionment. This article provides an impartial andfair overview of what the capabilities and the limitations ofLoRaWAN are. We discuss those in the context of use cases,and list open research and development questions.
Recolector de Cienci... arrow_drop_down Recolector de Ciencia Abierta, RECOLECTAArticleData sources: Recolector de Ciencia Abierta, RECOLECTAIEEE Communications MagazineOther literature type . Article . 2017 . Peer-reviewedLicense: IEEE CopyrightIEEE Communications MagazineArticle . 2017 . Peer-reviewedData sources: European Union Open Data Portalhttps://doi.org/10.48550/arxiv...Article . 2016License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/mcom.2017.1600613&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 827 citations 827 popularity Top 0.1% influence Top 0.1% impulse Top 0.01% Powered by BIP!more_vert Recolector de Cienci... arrow_drop_down Recolector de Ciencia Abierta, RECOLECTAArticleData sources: Recolector de Ciencia Abierta, RECOLECTAIEEE Communications MagazineOther literature type . Article . 2017 . Peer-reviewedLicense: IEEE CopyrightIEEE Communications MagazineArticle . 2017 . Peer-reviewedData sources: European Union Open Data Portalhttps://doi.org/10.48550/arxiv...Article . 2016License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/mcom.2017.1600613&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu
Loading
description Publicationkeyboard_double_arrow_right Article , Preprint , Other literature type 2021 Italy, Italy, Italy, Italy, Finland, Belgium, Italy, Italy, Netherlands, France, France, Italy, Italy, France, Switzerland, FrancePublisher:Cold Spring Harbor Laboratory Funded by:EC | SHOWCASEEC| SHOWCASERocchini; Duccio; Thouverai; Elisa; Marcantonio; Matteo; Iannacito; Martina; Da Re; Daniele; Torresani; Michele; Bacaro; Giovanni; Bazzichetto; Manuele; Bernardi; Alessandra; Foody; Giles M.; Furrer; Reinhard; Kleijn; David; Larsen; Stefano; Lenoir; Jonathan; Malavasi; Marco; Marchetto; Elisa; Messori; Filippo; Montaghi; Alessandro; Moudr'y; V'itvezslav; Naimi; Babak; Ricotta; Carlo; Rossini; Micol; Santi; Francesco; Santos; Maria J.; Schaepman; Michael E.; Schneider; Fabian D.; Schuh; Leila; Silvestri; Sonia; ?'imov'a; Petra; Skidmore; Andrew K.; Tattoni; Clara; Tordoni; Enrico; Vicario; Saverio; Zannini; Piero; Wegmann; Martin;doi: 10.1101/2021.02.09.430391 , 10.5281/zenodo.7081753 , 10.5167/uzh-205477 , 10.5281/zenodo.7081752 , 10.1111/2041-210x.13583
handle: 10449/67542 , 11368/2980231 , 2078.1/254199 , 11572/304627
pmid: 34262682
pmc: PMC8252722
doi: 10.1101/2021.02.09.430391 , 10.5281/zenodo.7081753 , 10.5167/uzh-205477 , 10.5281/zenodo.7081752 , 10.1111/2041-210x.13583
handle: 10449/67542 , 11368/2980231 , 2078.1/254199 , 11572/304627
pmid: 34262682
pmc: PMC8252722
AbstractEcosystem heterogeneity has been widely recognized as a key ecological feature, influencing several ecological functions, since it is strictly related to several ecological functions like diversity patterns and change, metapopulation dynamics, population connectivity, or gene flow.In this paper, we present a new R package - rasterdiv - to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns.The rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open source algorithms.
Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2021Full-Text: http://europepmc.org/articles/PMC8252722Data sources: PubMed CentralMethods in Ecology and EvolutionArticle . 2021Full-Text: https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2021/isi/skidmore_ras.pdfData sources: NARCISNARCIS; Research@WUROther literature type . Article . 2021License: CC BYFull-Text: https://edepot.wur.nl/547729HELDA - Digital Repository of the University of HelsinkiArticle . 2021 . Peer-reviewedData sources: HELDA - Digital Repository of the University of HelsinkiArchivio della ricerca- Università di Roma La SapienzaArticle . 2021Data sources: Archivio della ricerca- Università di Roma La Sapienzahttps://doi.org/10.1101/2021.0...Preprint . 2021add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1101/2021.02.09.430391&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 12 citations 12 popularity Top 10% influence Average impulse Top 10% Powered by BIP!visibility 19visibility views 19 download downloads 46 Powered bymore_vert Europe PubMed Centra... arrow_drop_down Europe PubMed CentralArticle . 2021Full-Text: http://europepmc.org/articles/PMC8252722Data sources: PubMed CentralMethods in Ecology and EvolutionArticle . 2021Full-Text: https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2021/isi/skidmore_ras.pdfData sources: NARCISNARCIS; Research@WUROther literature type . Article . 2021License: CC BYFull-Text: https://edepot.wur.nl/547729HELDA - Digital Repository of the University of HelsinkiArticle . 2021 . Peer-reviewedData sources: HELDA - Digital Repository of the University of HelsinkiArchivio della ricerca- Università di Roma La SapienzaArticle . 2021Data sources: Archivio della ricerca- Università di Roma La Sapienzahttps://doi.org/10.1101/2021.0...Preprint . 2021add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1101/2021.02.09.430391&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2020 Italy, France, Spain, FrancePublisher:Elsevier BV Funded by:EC | DREAMEC| DREAMLesort, Timothée; Lomonaco, Vincenzo; Stoian, Andrei; Maltoni, Davide; Filliat, David; Díaz-Rodríguez, Natalia;Abstract Continual learning (CL) is a particular machine learning paradigm where the data distribution and learning objective change through time, or where all the training data and objective criteria are never available at once. The evolution of the learning process is modeled by a sequence of learning experiences where the goal is to be able to learn new skills all along the sequence without forgetting what has been previously learned. CL can be seen as an online learning where knowledge fusion needs to take place in order to learn from streams of data presented sequentially in time. Continual learning also aims at the same time at optimizing the memory, the computation power and the speed during the learning process. An important challenge for machine learning is not necessarily finding solutions that work in the real world but rather finding stable algorithms that can learn in real world. Hence, the ideal approach would be tackling the real world in a embodied platform: an autonomous agent. Continual learning would then be effective in an autonomous agent or robot, which would learn autonomously through time about the external world, and incrementally develop a set of complex skills and knowledge.Robotic agents have to learn to adapt and interact with their environment using a continuous stream of observations. Some recent approaches aim at tackling continual learning for robotics, but most recent papers on continual learning only experiment approaches in simulation or with static datasets. Unfortunately, the evaluation of those algorithms does not provide insights on whether their solutions may help continual learning in the context of robotics. This paper aims at reviewing the existing state of the art of continual learning, summarizing existing benchmarks and metrics, and proposing a framework for presenting and evaluating both robotics and non robotics approaches in a way that makes transfer between both fields easier. We put light on continual learning in the context of robotics to create connections between fields and normalize approaches.
Information Fusion arrow_drop_down Repositorio Institucional Universidad de GranadaArticle . 2020Data sources: Repositorio Institucional Universidad de GranadaINRIA a CCSD electronic archive server; Mémoires en Sciences de l'Information et de la CommunicationArticle . 2019License: CC BY NCFull-Text: https://hal.science/hal-02381343/documenthttps://doi.org/10.48550/arxiv...Article . 2019License: arXiv Non-Exclusive DistributionData sources: DataciteArchivio della Ricerca - Università di PisaArticle . 2020Data sources: Archivio della Ricerca - Università di Pisaadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.inffus.2019.12.004&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 188 citations 188 popularity Top 0.1% influence Top 1% impulse Top 0.1% Powered by BIP!more_vert Information Fusion arrow_drop_down Repositorio Institucional Universidad de GranadaArticle . 2020Data sources: Repositorio Institucional Universidad de GranadaINRIA a CCSD electronic archive server; Mémoires en Sciences de l'Information et de la CommunicationArticle . 2019License: CC BY NCFull-Text: https://hal.science/hal-02381343/documenthttps://doi.org/10.48550/arxiv...Article . 2019License: arXiv Non-Exclusive DistributionData sources: DataciteArchivio della Ricerca - Università di PisaArticle . 2020Data sources: Archivio della Ricerca - Università di Pisaadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.inffus.2019.12.004&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2019 Netherlands, Netherlands, Netherlands, FrancePublisher:Springer Science and Business Media LLC Funded by:EC | ReMIXEC| ReMIXAuthors: Noémie Gaudio; Abraham J. Escobar-Gutiérrez; Pierre Casadebaig; Jochem B. Evers; +17 AuthorsNoémie Gaudio; Abraham J. Escobar-Gutiérrez; Pierre Casadebaig; Jochem B. Evers; Frédéric Gérard; Gaëtan Louarn; Nathalie Colbach; Sebastian Munz; Marie Launay; Hélène Marrou; Romain Barillot; Philippe Hinsinger; Jacques Eric Bergez; Didier Combes; Jean-Louis Durand; Ela Frak; Loïc Pagès; Christophe Pradal; Sébastien Saint-Jean; Wopke van der Werf; Eric Justes;Growing mixtures of annual arable crop species or genotypes is a promising way to improve crop production without increasing agricultural inputs. To design optimal crop mixtures, choices of species, genotypes, sowing proportion, plant arrangement, and sowing date need to be made but field experiments alone are not sufficient to explore such a large range of factors. Crop modeling allows to study, understand and ultimately design cropping systems and is an established method for sole crops. Recently, modeling started to be applied to annual crop mixtures as well. Here, we review to what extent crop simulation models and individual-based models are suitable to capture and predict the specificities of annual crop mixtures. We argued that: 1) The crop mixture spatio-temporal heterogeneity (influencing the occurrence of ecological processes) determines the choice of the modeling approach (plant or crop centered). 2) Only few crop models (adapted from sole crop models) and individual-based models currently exist to simulate annual crop mixtures. 3) Crop models are mainly used to address issues related to crop mixtures management and to the integration of crop mixtures into larger scales such as the rotation, whereas individual-based models are mainly used to identify plant traits involved in crop mixture performance and to quantify the relative contribution of the different ecological processes (niche complementarity, facilitation, competition, plasticity) to crop mixture functioning. This review highlights that modeling of annual crop mixtures is in its infancy and gives to model users some important keys to choose the model based on the questions they want to answer, with awareness of the strengths and weaknesses of each of the modeling approaches. Comment: 42 pages, 5 figures
Agritrop arrow_drop_down Research@WUR; Agronomy for Sustainable DevelopmentOther literature type . Article . 2019 . Peer-reviewedLicense: Springer TDMHAL - UPEC / UPEM; HAL-Pasteur; HAL-Inserm; Hal-DiderotArticle . 2019Full-Text: https://hal.inria.fr/hal-02228974/documentadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/s13593-019-0562-6&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 74 citations 74 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!more_vert Agritrop arrow_drop_down Research@WUR; Agronomy for Sustainable DevelopmentOther literature type . Article . 2019 . Peer-reviewedLicense: Springer TDMHAL - UPEC / UPEM; HAL-Pasteur; HAL-Inserm; Hal-DiderotArticle . 2019Full-Text: https://hal.inria.fr/hal-02228974/documentadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1007/s13593-019-0562-6&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Preprint , Article 2018 Spain, FrancePublisher:Elsevier BV Funded by:EC | DREAMEC| DREAMAuthors: Timothée Lesort; Natalia Díaz-Rodríguez; Jean-Francois Goudou; David Filliat;Timothée Lesort; Natalia Díaz-Rodríguez; Jean-Francois Goudou; David Filliat;International audience; Representation learning algorithms are designed to learn abstract features that characterize data. State representation learning (SRL) focuses on a particular kind of representation learning where learned features are in low dimension, evolve through time, and are influenced by actions of an agent. The representation is learned to capture the variation in the environment generated by the agent's actions; this kind of representation is particularly suitable for robotics and control scenarios. In particular, the low dimension characteristic of the representation helps to overcome the curse of dimensionality, provides easier interpretation and utilization by humans and can help improve performance and speed in policy learning algorithms such as reinforcement learning. This survey aims at covering the state-of-the-art on state representation learning in the most recent years. It reviews different SRL methods that involve interaction with the environment , their implementations and their applications in robotics control tasks (simulated or real). In particular, it highlights how generic learning objectives are differently exploited in the reviewed algorithms. Finally, it discusses evaluation methods to assess the representation learned and summarizes current and future lines of research.
arXiv.org e-Print Ar... arrow_drop_down Repositorio Institucional Universidad de GranadaArticle . 2018Data sources: Repositorio Institucional Universidad de Granadahttps://doi.org/10.48550/arxiv...Article . 2018License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.neunet.2018.07.006&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 134 citations 134 popularity Top 1% influence Top 1% impulse Top 1% Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down Repositorio Institucional Universidad de GranadaArticle . 2018Data sources: Repositorio Institucional Universidad de Granadahttps://doi.org/10.48550/arxiv...Article . 2018License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.neunet.2018.07.006&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Preprint , Article 2018 FrancePublisher:IEEE Funded by:EC | An.Dy, EC | ResiBotsEC| An.Dy ,EC| ResiBotsAuthors: Chatzilygeroudis, Konstantinos; Mouret, Jean-Baptiste;Chatzilygeroudis, Konstantinos; Mouret, Jean-Baptiste;The most data-efficient algorithms for reinforcement learning in robotics are model-based policy search algorithms, which alternate between learning a dynamical model of the robot and optimizing a policy to maximize the expected return given the model and its uncertainties. Among the few proposed approaches, the recently introduced Black-DROPS algorithm exploits a black-box optimization algorithm to achieve both high data-efficiency and good computation times when several cores are used; nevertheless, like all model-based policy search approaches, Black-DROPS does not scale to high dimensional state/action spaces. In this paper, we introduce a new model learning procedure in Black-DROPS that leverages parameterized black-box priors to (1) scale up to high-dimensional systems, and (2) be robust to large inaccuracies of the prior information. We demonstrate the effectiveness of our approach with the "pendubot" swing-up task in simulation and with a physical hexapod robot (48D state space, 18D action space) that has to walk forward as fast as possible. The results show that our new algorithm is more data-efficient than previous model-based policy search algorithms (with and without priors) and that it can allow a physical 6-legged robot to learn new gaits in only 16 to 30 seconds of interaction time. Accepted at ICRA 2018; 8 pages, 4 figures, 2 algorithms, 1 table; Video at https://youtu.be/HFkZkhGGzTo ; Spotlight ICRA presentation at https://youtu.be/_MZYDhfWeLc
http://arxiv.org/pdf... arrow_drop_down Hal-DiderotConference object . 2018Full-Text: https://hal.inria.fr/hal-01768285/documentData sources: Hal-Diderothttps://doi.org/10.1109/icra.2...Other literature type . Conference object . 2018 . Peer-reviewedhttps://doi.org/10.48550/arxiv...Article . 2017License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/icra.2018.8461083&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 16 citations 16 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!more_vert http://arxiv.org/pdf... arrow_drop_down Hal-DiderotConference object . 2018Full-Text: https://hal.inria.fr/hal-01768285/documentData sources: Hal-Diderothttps://doi.org/10.1109/icra.2...Other literature type . Conference object . 2018 . Peer-reviewedhttps://doi.org/10.48550/arxiv...Article . 2017License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/icra.2018.8461083&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Other literature type , Article , Preprint 2018Embargo end date: 01 Jan 2018 France, Germany, SwitzerlandPublisher:arXiv Funded by:EC | An.Dy, EC | RISE, CHIST-ERA | Heap +2 projectsEC| An.Dy ,EC| RISE ,CHIST-ERA| Heap ,EC| ResiBots ,EC| MEMMOAuthors: Konstantinos Chatzilygeroudis; Vassilis Vassiliades; Freek Stulp; Sylvain Calinon; +1 AuthorsKonstantinos Chatzilygeroudis; Vassilis Vassiliades; Freek Stulp; Sylvain Calinon; Jean-Baptiste Mouret;Most policy search algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot. This survey article focuses on the extreme other end of the spectrum: how can a robot adapt with only a handful of trials (a dozen) and a few minutes? By analogy with the word "big-data", we refer to this challenge as "micro-data reinforcement learning". We show that a first strategy is to leverage prior knowledge on the policy structure (e.g., dynamic movement primitives), on the policy parameters (e.g., demonstrations), or on the dynamics (e.g., simulators). A second strategy is to create data-driven surrogate models of the expected reward (e.g., Bayesian optimization) or the dynamical model (e.g., model-based policy search), so that the policy optimizer queries the model instead of the real system. Overall, all successful micro-data algorithms combine these two strategies by varying the kind of model and prior knowledge. The current scientific challenges essentially revolve around scaling up to complex robots (e.g., humanoids), designing generic priors, and optimizing the computing time. Comment: 21 pages, 3 figures, 4 algorithms, accepted at IEEE Transactions on Robotics
IEEE Transactions on... arrow_drop_down IEEE Transactions on Robotics; DLR publication serverOther literature type . Article . 2019License: CC BYInfoscience - EPFL scientific publicationsOther literature typeData sources: Infoscience - EPFL scientific publicationsHAL - UPEC / UPEM; HAL-Pasteur; HAL-Inserm; Hal-DiderotArticle . 2020Full-Text: https://hal.inria.fr/hal-02393432/documentadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.48550/arxiv.1807.02303&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!visibility 43visibility views 43 download downloads 43 Powered bymore_vert IEEE Transactions on... arrow_drop_down IEEE Transactions on Robotics; DLR publication serverOther literature type . Article . 2019License: CC BYInfoscience - EPFL scientific publicationsOther literature typeData sources: Infoscience - EPFL scientific publicationsHAL - UPEC / UPEM; HAL-Pasteur; HAL-Inserm; Hal-DiderotArticle . 2020Full-Text: https://hal.inria.fr/hal-02393432/documentadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.48550/arxiv.1807.02303&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Article , Preprint 2017 FrancePublisher:IEEE Funded by:EC | RoMaNSEC| RoMaNSAuthors: Ghalamzan, Amir, M; Abi-Farraj, Firas; Robuffo Giordano, Paolo; Stolkin, Rustam;Ghalamzan, Amir, M; Abi-Farraj, Firas; Robuffo Giordano, Paolo; Stolkin, Rustam;This paper addresses the problem of mixed initiative, shared control for master-slave grasping and manipulation. We propose a novel system, in which an autonomous agent assists a human in teleoperating a remote slave arm/gripper, using a haptic master device. Our system is designed to exploit the human operator's expertise in selecting stable grasps (still an open research topic in autonomous robotics). Meanwhile, a-priori knowledge of: i) the slave robot kinematics, and ii) the desired post-grasp manipulative trajectory, are fed to an autonomous agent which transmits force cues to the human, to encourage maximally manipulable grasp pose selections. Specifically, the autonomous agent provides force cues to the human, during the reach-to-grasp phase, which encourage the human to select grasp poses which maximise manipulation capability during the post-grasp object manipulation phase. We introduce a task-relevant velocity manipulability cost function (TOV), which is used to identify the maximum kinematic capability of a manipulator during post-grasp motions, and feed this back as force cues to the human during the pre-grasp phase. We show that grasps which minimise TOV result in significantly reduced control effort of the manipulator, compared to other feasible grasps. We demonstrate the effectiveness of our approach by experiments with both real and simulated robots. Comment: To be appeared in IEEE/RAS IROS 2017
arXiv.org e-Print Ar... arrow_drop_down HAL-Rennes 1; Hal-DiderotConference object . 2017Full-Text: https://hal.inria.fr/hal-01572347/documenthttps://doi.org/10.48550/arxiv...Article . 2017License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/iros.2017.8206178&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 19 citations 19 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down HAL-Rennes 1; Hal-DiderotConference object . 2017Full-Text: https://hal.inria.fr/hal-01572347/documenthttps://doi.org/10.48550/arxiv...Article . 2017License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/iros.2017.8206178&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Conference object , Article , Preprint , Other literature type 2017 FrancePublisher:IEEE Funded by:EC | An.Dy, EC | ResiBotsEC| An.Dy ,EC| ResiBotsChatzilygeroudis, Konstantinos; Rama, Roberto; Kaushik, Rituraj; Goepp, Dorian; Vassiliades, Vassilis; Mouret, Jean-Baptiste;The most data-efficient algorithms for reinforcement learning (RL) in robotics are based on uncertain dynamical models: after each episode, they first learn a dynamical model of the robot, then they use an optimization algorithm to find a policy that maximizes the expected return given the model and its uncertainties. It is often believed that this optimization can be tractable only if analytical, gradient-based algorithms are used; however, these algorithms require using specific families of reward functions and policies, which greatly limits the flexibility of the overall approach. In this paper, we introduce a novel model-based RL algorithm, called Black-DROPS (Black-box Data-efficient RObot Policy Search) that: (1) does not impose any constraint on the reward function or the policy (they are treated as black-boxes), (2) is as data-efficient as the state-of-the-art algorithm for data-efficient RL in robotics, and (3) is as fast (or faster) than analytical approaches when several cores are available. The key idea is to replace the gradient-based optimization algorithm with a parallel, black-box algorithm that takes into account the model uncertainties. We demonstrate the performance of our new algorithm on two standard control benchmark problems (in simulation) and a low-cost robotic manipulator (with a real robot). Comment: Accepted at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2017; Code at http://github.com/resibots/blackdrops; Video at http://youtu.be/kTEyYiIFGPM
http://arxiv.org/pdf... arrow_drop_down Hal-DiderotConference object . 2017Full-Text: https://hal.inria.fr/hal-01576683/documentData sources: Hal-Diderothttps://doi.org/10.1109/iros.2...Conference object . 2017 . Peer-reviewedhttps://doi.org/10.48550/arxiv...Article . 2017License: arXiv Non-Exclusive DistributionData sources: Datacitehttps://doi.org/10.1109/IROS.2...Other literature type . Conference object . 2017Data sources: European Union Open Data Portaladd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/iros.2017.8202137&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 48 citations 48 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!more_vert http://arxiv.org/pdf... arrow_drop_down Hal-DiderotConference object . 2017Full-Text: https://hal.inria.fr/hal-01576683/documentData sources: Hal-Diderothttps://doi.org/10.1109/iros.2...Conference object . 2017 . Peer-reviewedhttps://doi.org/10.48550/arxiv...Article . 2017License: arXiv Non-Exclusive DistributionData sources: Datacitehttps://doi.org/10.1109/IROS.2...Other literature type . Conference object . 2017Data sources: European Union Open Data Portaladd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/iros.2017.8202137&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2017 France, Spain, France, FrancePublisher:Institute of Electrical and Electronics Engineers (IEEE) Funded by:EC | ARMOUR, EC | F-InteropEC| ARMOUR ,EC| F-InteropAdelantado, Ferran; Vilajosana, Xavier; Tuset-Peiro, Pere; Martinez, Borja; Melia, Joan; Watteyne, Thomas;handle: 10609/93072
International audience; Low-Power Wide Area Networking (LPWAN) technologyoffers long-range communication, which enables new typesof services. Several solutions exist; LoRaWAN is arguablethe most adopted. It promises ubiquitous connectivity inoutdoor IoT applications, while keeping network structures,and management, simple. This technology has received a lotof attention in recent months from network operators andsolution providers. Yet, the technology has limitations thatneed to be clearly understood to avoid inflated expectationsand disillusionment. This article provides an impartial andfair overview of what the capabilities and the limitations ofLoRaWAN are. We discuss those in the context of use cases,and list open research and development questions.
Recolector de Cienci... arrow_drop_down Recolector de Ciencia Abierta, RECOLECTAArticleData sources: Recolector de Ciencia Abierta, RECOLECTAIEEE Communications MagazineOther literature type . Article . 2017 . Peer-reviewedLicense: IEEE CopyrightIEEE Communications MagazineArticle . 2017 . Peer-reviewedData sources: European Union Open Data Portalhttps://doi.org/10.48550/arxiv...Article . 2016License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/mcom.2017.1600613&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 827 citations 827 popularity Top 0.1% influence Top 0.1% impulse Top 0.01% Powered by BIP!more_vert Recolector de Cienci... arrow_drop_down Recolector de Ciencia Abierta, RECOLECTAArticleData sources: Recolector de Ciencia Abierta, RECOLECTAIEEE Communications MagazineOther literature type . Article . 2017 . Peer-reviewedLicense: IEEE CopyrightIEEE Communications MagazineArticle . 2017 . Peer-reviewedData sources: European Union Open Data Portalhttps://doi.org/10.48550/arxiv...Article . 2016License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/mcom.2017.1600613&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu