RobotLearn is a research team at Inria Grenoble, created on July 1st, 2021. We aim to contribute to the development of robotic social intelligence.
Grenoble, France
Joined July 2021
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RobotLearn Research Team @ Inria Grenoble retweeted
Last Early Bird spots available (Dec 23rd) to hear Prof. Magalie Ochs' keynote at SoRAIM. Check project.inria.fr/soraim/
RobotLearn Research Team @ Inria Grenoble retweeted
Doing a PhD/postdoc in computer vision or NLP? Interested in multimodal conversational AI and social robotics? Register now for SoRAIM 2026, a unique opportunity to delve into SoTA research across all these disciplines, with inspiring keynote speakers and hands-on sessions.
What a speaker line up! Register (project.inria.fr/soraim/) if you want to hear these wonderful people.
RobotLearn Research Team @ Inria Grenoble retweeted
Hey there! We are organising a wonderful Winter School in the French Alps! Check it out!!!
with @thomashueber @Steph_lat Olivier Perrotin and Laurent Girin, supported by @inria_grenoble @GipsaLab @CNRS @UGrenobleAlpes @MIAI_UGA @PALRobotics @gdr_tal_cnrs @RobotLearnInria
What a speaker line up! Register (project.inria.fr/soraim/) if you want to hear these wonderful people.
RobotLearn Research Team @ Inria Grenoble retweeted
[#PhD 👩🎓]
Félicitations à Xiaoyu Lin (équipe @RobotLearnInria) qui a soutenu sa thèse "Modèles génératifs à variables latentes profondes pour le traitement multimédia" #deeplearning 🤖
➕ d'infos :
theses.fr/s256317
inria.fr/fr/robotlearn
PhD Position Open in Deep Information Geometry.
At the RobotLearn team, and together with @plc_rodrigues from @InriaStatify and @xavirema we are looking for a PhD candidate motivated to explore deep information geometry. Please apply following the link below.
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Open internship position at our team on "Social Robotics with ChatGPT". Check: team.inria.fr/robotlearn/mas…
Open intership position at our team on "Deep Statistical Manifolds". Check: team.inria.fr/robotlearn/mas…
Are you working on RL in the Grenoble-Lyon area? This might be interesting: team.inria.fr/robotlearn/rl-… #reinforcementlearning #rl @inria_grenoble @MIAI_UGA
Màster internship proposal in our team at @inria_grenoble!
Master Internship about Automated Discovery of Moving Lifeforms in Celluar Automata (#Lenia) using #DeepLearning.
At @RobotLearnInria.
Start: Beginning of 2023
Duration: 5 - 6 months
Learn more at:
team.inria.fr/robotlearn/job…
ALT A simulated organism in Lenia. Tken from: https://github.com/Chakazul/Lenia
Si vous voulez en savoir plus à propos du projet @SpringH2020 et de comment les robots socialement intelligents peuvent être appliqués à la gérontologie, c'est par ici :
C'est la semaine de l' #Europe 🇪🇺 ! Pour l'occasion (re)découvrez notre article sur @SpringH2020 : un projet multidisciplinaire et européen sur la robotique sociale🤖
Avec @xavirema et @RobotLearnInria
➡ inria.fr/fr/spring-robotique…
RobotLearn Research Team @ Inria Grenoble retweeted
Only 10 days left to apply to this assistant/associate professor position in our team!
Associate/assistant professor (maitre de conf) position to work on Learning for Video Processing and Computer Vision at @telecomparis @IP_Paris_. Details available at: institutminestelecom.recruit….
We are very happy to announce that the RobotLearn team has three papers accepted at CVPR 2022! Congratulations to the PhD students @GUO_Wen_, @BieXiaoyu, Hanyu Xuan, @DonkeyShot21, and @victorturrisi, and to our collaborators @fmorenoguer @eliricci_ @inthebrownbag @julienmairal.
Finally, we looked into adapting self-supervised methods to the continual learning setting, by adding a predictor that maps previous features into the new feature space. arxiv.org/abs/2112.04215
Following our trend on using dynamic variational autoencoders, we propose an unsupervised speech enhancement algorithm based on #dvae. @BieXiaoyu @SimonLeglaive @xavirema and Laurent Girin. @inria_grenoble @UGrenobleAlpes @MIAI_UGA @SpringH2020
Happy new year to everyone from the RobotLearn team. Over the past year, we have achieved several contributions that we would like to summarise. #teamwork @MIAI_UGA @inria_grenoble @UGrenobleAlpes @SpringH2020
We have also contributed to the use of neural episodic control in combination with linear successor features (team.inria.fr/robotlearn/suc…).