no code implementations • 11 Apr 2024 • Xavier Alameda-Pineda, Angus Addlesee, Daniel Hernández García, Chris Reinke, Soraya Arias, Federica Arrigoni, Alex Auternaud, Lauriane Blavette, Cigdem Beyan, Luis Gomez Camara, Ohad Cohen, Alessandro Conti, Sébastien Dacunha, Christian Dondrup, Yoav Ellinson, Francesco Ferro, Sharon Gannot, Florian Gras, Nancie Gunson, Radu Horaud, Moreno D'Incà, Imad Kimouche, Séverin Lemaignan, Oliver Lemon, Cyril Liotard, Luca Marchionni, Mordehay Moradi, Tomas Pajdla, Maribel Pino, Michal Polic, Matthieu Py, Ariel Rado, Bin Ren, Elisa Ricci, Anne-Sophie Rigaud, Paolo Rota, Marta Romeo, Nicu Sebe, Weronika Sieińska, Pinchas Tandeitnik, Francesco Tonini, Nicolas Turro, Timothée Wintz, Yanchao Yu
Despite the many recent achievements in developing and deploying social robotics, there are still many underexplored environments and applications for which systematic evaluation of such systems by end-users is necessary.
1 code implementation • 7 Nov 2023 • Daniel Jost, Basavasagar Patil, Xavier Alameda-Pineda, Chris Reinke
Deep Neural Networks (DNNs) became the standard tool for function approximation with most of the introduced architectures being developed for high-dimensional input data.
no code implementations • 7 Jun 2022 • Anand Ballou, Xavier Alameda-Pineda, Chris Reinke
We demonstrate the interest of the RBF layer and the usage of meta-RL for social robotics on four robotic simulation tasks.
no code implementations • 4 Nov 2021 • David Emukpere, Xavier Alameda-Pineda, Chris Reinke
A longstanding goal in reinforcement learning is to build intelligent agents that show fast learning and a flexible transfer of skills akin to humans and animals.
no code implementations • 29 Oct 2021 • Chris Reinke, Xavier Alameda-Pineda
Successor Representations (SR) and their extension Successor Features (SF) are prominent transfer mechanisms in domains where reward functions change between tasks.
no code implementations • 13 May 2020 • Mayalen Etcheverry, Pierre-Yves Oudeyer, Chris Reinke
A central challenge is how to learn incrementally representations in order to progressively build a map of the discovered structures and re-use it to further explore.
no code implementations • 18 Apr 2020 • Chris Reinke
Reinforcement learning (RL) allows to solve complex tasks such as Go often with a stronger performance than humans.
no code implementations • ICLR 2020 • Chris Reinke, Mayalen Etcheverry, Pierre-Yves Oudeyer
Using a continuous GOL as a testbed, we show that recent intrinsically-motivated machine learning algorithms (POP-IMGEPs), initially developed for learning of inverse models in robotics, can be transposed and used in this novel application area.