| Year |
Citation |
Score |
| 2022 |
Renaudo E, Zech P, Chatila R, Khamassi M. Editorial: Computational models of affordance for robotics. Frontiers in Neurorobotics. 16: 1045355. PMID 36277333 DOI: 10.3389/fnbot.2022.1045355 |
0.719 |
|
| 2019 |
Zech P, Renaudo E, Haller S, Zhang X, Piater J. Action representations in robotics: A taxonomy and systematic classification The International Journal of Robotics Research. 38: 518-562. DOI: 10.1177/0278364919835020 |
0.658 |
|
| 2018 |
Chatila R, Renaudo E, Andries M, Chavez-Garcia RO, Luce-Vayrac P, Gottstein R, Alami R, Clodic A, Devin S, Girard B, Khamassi M. Toward Self-Aware Robots. Frontiers in Robotics and Ai. 5: 88. PMID 33500967 DOI: 10.3389/frobt.2018.00088 |
0.715 |
|
| 2018 |
Chatila R, Renaudo E, Andries M, Chavez-Garcia R, Luce-Vayrac P, Gottstein R, Alami R, Clodic A, Devin S, Girard B, Khamassi M. Toward Self-Aware Robots Frontiers in Robotics and Ai. 5. DOI: 10.3389/frobt.2018.00088 |
0.693 |
|
| 2016 |
Réguigne-Khamassi M, Girard B, Clodic A, Devin S, Renaudo E, Pacherie E, Alami R, Chatila R. Integration of Action, Joint Action and Learning in Robotics Cognitive Architectures Intellectica. Revue De L'Association Pour La Recherche Cognitive. 65: 169-203. DOI: 10.3406/Intel.2016.1794 |
0.757 |
|
| 2015 |
Renaudo E, Girard B, Chatila R, Khamassi M. Respective Advantages and Disadvantages of Model-based and Model-free Reinforcement Learning in a Robotics Neuro-inspired Cognitive Architecture Procedia Computer Science. 71: 178-184. DOI: 10.1016/J.Procs.2015.12.194 |
0.712 |
|
| 2014 |
Renaudo E, Girard B, Chatila R, Khamassi M. Design of a control architecture for habit learning in robots Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8608: 249-260. DOI: 10.1007/978-3-319-09435-9_22 |
0.713 |
|
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