Design of a Control Architecture for Habit Learning in Robots. - Erwan Renaudo; Benoît Girard 001; Raja Chatila 001; Mehdi Khamassi (Living Machines, 2014)

Respective Advantages and Disadvantages of Model-based and Model-free Reinforcement Learning in a Robotics Neuro-inspired Cognitive Architecture. - Erwan Renaudo; Benoît Girard 001; Raja Chatila 001; Mehdi Khamassi (BICA, 2015)

Which criteria for autonomously shifting between goal-directed and habitual behaviors in robots? - Erwan Renaudo; Benoît Girard 001; Raja Chatila 001; Mehdi Khamassi (ICDL-EPIROB, 2015)

Des comportements flexibles aux comportements habituels : meta-apprentissage neuro-inspiré pour la robotique autonome. (From flexible to habitual behaviors : neuro-inspired meta-learning for autonomous robots). - Erwan Renaudo (2016)

Toward Self-Aware Robots. - Raja Chatila 001; Erwan Renaudo; Mihai Andries; Ricardo Omar Chávez García; Pierre Luce-Vayrac; Raphaël Gottstein; Rachid Alami 001; Aurélie Clodic; Sandra Devin; Benoît Girard 001; Mehdi Khamassi (Frontiers Robotics AI, 2018)

Action representations in robotics: A taxonomy and systematic classification. - Philipp Zech; Erwan Renaudo; Simon Haller; Xiang Zhang; Justus H. Piater (Int. J. Robotics Res., 2019)

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. - Rémi Dromnelle; Erwan Renaudo; Guillaume Pourcel; Raja Chatila 001; Benoît Girard 001; Mehdi Khamassi (Living Machines, 2020)

ROSSINI: RobOt kidS deSIgn thiNkIng. - Simon Haller-Seeber; Erwan Renaudo; Philipp Zech; Florian Westreicher; Markus Walzthöni; Stefan Strappler; Cornelia Vidovic; Justus H. Piater (RiE, 2020)

Coping with the variability in humans reward during simulated human-robot interactions through the coordination of multiple learning strategies. - Rémi Dromnelle; Benoît Girard 001; Erwan Renaudo; Raja Chatila 001; Mehdi Khamassi (RO-MAN, 2020)

A Visual Intelligence Scheme for Hard Drive Disassembly in Automated Recycling Routines. - Erenus Yildiz; Tobias Brinker; Erwan Renaudo; Jakob J. Hollenstein; Simon Haller-Seeber; Justus H. Piater; Florentin Wörgötter (ROBOVIS, 2020)

Improving the Exploration of Deep Reinforcement Learning in Continuous Domains using Planning for Policy Search. - Jakob J. Hollenstein; Erwan Renaudo; Matteo Saveriano; Justus H. Piater (CoRR, 2020)

How do Offline Measures for Exploration in Reinforcement Learning behave? - Jakob J. Hollenstein; Sayantan Auddy; Matteo Saveriano; Erwan Renaudo; Justus H. Piater (CoRR, 2020)

Deep Learning for Fast Segmentation of E-waste Devices' Inner Parts in a Recycling Scenario. - Cristof Rojas; Antonio Jose Rodríguez-Sánchez; Erwan Renaudo (ICPRAI (1), 2022)

Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance. - Jakob J. Hollenstein; Sayantan Auddy; Matteo Saveriano; Erwan Renaudo; Justus H. Piater (Trans. Mach. Learn. Res., 2022)

Editorial: Computational models of affordance for robotics. - Erwan Renaudo; Philipp Zech; Raja Chatila 001; Mehdi Khamassi (Frontiers Neurorobotics, 2022)

Reducing Computational Cost During Robot Navigation and Human-Robot Interaction with a Human-Inspired Reinforcement Learning Architecture. - Rémi Dromnelle; Erwan Renaudo; Mohamed Chetouani; Petros Maragos; Raja Chatila 001; Benoît Girard 001; Mehdi Khamassi (Int. J. Soc. Robotics, 2023)

Unsupervised Learning of Effective Actions in Robotics. - Marko Zaric; Jakob J. Hollenstein; Justus H. Piater; Erwan Renaudo (CoRR, 2024)