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Design of a control architecture for habit learning in robots
E Renaudo, B Girard, R Chatila, M Khamassi
Conference on Biomimetic and Biohybrid Systems, 249-260, 2014
Toward self-aware robots
R Chatila, E Renaudo, M Andries, RO Chavez-Garcia, P Luce-Vayrac, ...
Frontiers in Robotics and AI 5, 88, 2018
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
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
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
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
Des comportements flexibles aux comportements habituels: Meta-apprentissage neuro-inspiré pour la robotique autonome
E Renaudo
Université Pierre et Marie Curie (Paris 6), 2016
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
arXiv preprint arXiv:2004.14698, 2020
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
arXiv preprint arXiv:2005.03987, 2020
Improving Exploration of Deep Reinforcement Learning using Planning for Policy Search
JJ Hollenstein, E Renaudo, J Piater
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