| Toward self-aware robots R Chatila, E Renaudo, M Andries, RO Chavez-Garcia, P Luce-Vayrac, ... Frontiers in Robotics and AI 5, 88, 2018 | 77 | 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 | 33 | 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 | 27 | 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 | 22 | 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 | 18 | 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 | 17 | 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 | 14 | 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 | 13 | 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 | 10 | 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 | 9 | 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, ... ROBOVIS, 2020 | 8 | 2020 |
| 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 | 6 | 2023 |
| Computational models of affordance for robotics E Renaudo, P Zech, R Chatila, M Khamassi Frontiers in Neurorobotics 16, 1045355, 2022 | 4 | 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 | 3 | 2020 |
| Improving Exploration of Deep Reinforcement Learning using Planning for Policy Search JJ Hollenstein, E Renaudo, J Piater | 3 | 2019 |
| 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 | 2 | 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 | 2 | 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 | 2 | 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 | 1 | 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 | 1 | 2020 |
Mehdi KhamassiCNRS research director, Sorbonne Université在 isir.upmc.fr 的电子邮件经过验证
