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Toward self-aware robots
R Chatila, E Renaudo, M Andries, RO Chavez-Garcia, P Luce-Vayrac, ...
Frontiers in Robotics and AI 5, 88, 2018
Design of a control architecture for habit learning in robots
E Renaudo, B Girard, R Chatila, M Khamassi
Biomimetic and Biohybrid Systems: Third International Conference, Living …, 2014
Respective Advantages and Disadvantages of Model-based and Model-free Reinforcement Learning in a Robotics Neuro-inspired Cognitive Architecture
E Renaudo, B Girard, R Chatila, M Khamassi
Procedia Computer Science 71, 178-184, 2015
Action representations in robotics: A taxonomy and systematic classification
P Zech, E Renaudo, S Haller, X Zhang, J Piater
The International Journal of Robotics Research 38 (5), 518-562, 2019
Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance
J Hollenstein, S Auddy, M Saveriano, E Renaudo, J Piater
arXiv preprint arXiv:2206.03787, 2022
Integration of Action, Joint Action and Learning in Robot Cognitive Architectures
M Khamassi, B Girard, A Clodic, S Devin, E Renaudo, E Pacherie, ...
Intellectica-La revue de l’Association pour la Recherche sur les sciences de …, 2016
Which criteria for autonomously shifting between goal-directed and habitual behaviors in robots?
E Renaudo, B Girard, R Chatila, M Khamassi
ICDL-EpiRob 2015, 2015
How to reduce computation time while sparing performance during robot navigation? A neuro-inspired architecture for autonomous shifting between model-based and model-free learning
R Dromnelle, E Renaudo, G Pourcel, R Chatila, B Girard, M Khamassi
Conference on Biomimetic and Biohybrid Systems, 68-79, 2020
Learning to interact with humans using goal-directed and habitual behaviors
E Renaudo, S Devin, B Girard, R Chatila, R Alami, M Khamassi, A Clodic
Ro-Man 2015, Workshop on Learning for Human-Robot Collaboration, 2015
Coping with the variability in humans reward during simulated human-robot interactions through the coordination of multiple learning strategies*
R Dromnelle, B Girard, E Renaudo, R Chatila, M Khamassi
2020 29th IEEE International Conference on Robot and Human Interactive …, 2020
A Visual Intelligence Scheme for Hard Drive Disassembly in Automated Recycling Routines
E Yildiz, T Brinker, E Renaudo, JJ Hollenstein, S Haller-Seeber, J Piater, ...
Reducing computational cost during robot navigation and human–robot interaction with a human-inspired reinforcement learning architecture
R Dromnelle, E Renaudo, M Chetouani, P Maragos, R Chatila, B Girard, ...
International Journal of Social Robotics 15 (8), 1297-1323, 2023
Computational models of affordance for robotics
E Renaudo, P Zech, R Chatila, M Khamassi
Frontiers in Neurorobotics 16, 1045355, 2022
ROSSINI: RobOt kidS deSIgn thiNkIng
S Haller-Seeber, E Renaudo, P Zech, F Westreicher, M Walzthöni, ...
International Conference on Robotics in Education (RiE), 16-25, 2020
Improving Exploration of Deep Reinforcement Learning using Planning for Policy Search
JJ Hollenstein, E Renaudo, J Piater
How does the type of exploration-noise affect returns and exploration on Reinforcement Learning benchmarks
J Hollenstein, M Saveriano, A Sayantan, E Renaudo, J Piater
Austrian Robotics Workshop, 22-26, 2021
How do Offline Measures for Exploration in Reinforcement Learning behave?
JJ Hollenstein, S Auddy, M Saveriano, E Renaudo, J Piater
arXiv preprint arXiv:2010.15533, 2020
Des comportements flexibles aux comportements habituels: Meta-apprentissage neuro-inspiré pour la robotique autonome
E Renaudo
Université Pierre et Marie Curie (Paris 6), 2016
Improving the Exploration of Deep Reinforcement Learning in Continuous Domains using Planning for Policy Search
JJ Hollenstein, E Renaudo, M Saveriano, J Piater
arXiv preprint arXiv:2010.12974, 2020
How Does Explicit Exploration Influence Deep Reinforcement Learning
JJ Hollenstein, E Renaudo, S Matteo, J Piater
Joint Austrian Computer Vision and Robotics Workshop, 29-30, 2020
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