| Abhinav Valada |
Robot Learning Lab |
Freiburg im Breisgau, Germany |
robotics |
robot learning, robot perception, state estimation, planning |
| Alexander Mathis |
Alexander Mathis Group of Computational Neuroscience & AI |
Geneva, Switzerland |
neuroRL |
brain-inspired reinforcement learning, behavioural representation learning, sensorimotor control, skill learning |
| Andreas Krause |
Learning & Adaptive Systems Group |
Zurich, Switzerland |
RL theory |
online learning, active learning, reinforcement learning, bandits |
| Andrew Saxe |
Saxe Lab |
London, United Kingdom |
neuroRL |
theory of learning, deep learning dynamics, neural network theory, representation learning |
| Angela Schoellig |
Learning Systems and Robotics Lab |
Munich, Germany |
robotics |
safe reinforcement learning, learning-based control, control theory, robot learning |
| Aude Billard |
Learning Algorithms and Systems Laboratory |
Lausanne, Switzerland |
robotics |
robot learning, human-robot interaction, dexterous manipulation, adaptive control |
| Auke Ijspeert |
Biorobotics Laboratory (BioRob) |
Lausanne, Switzerland |
robotics |
bio-inspired robotics, locomotion control, central pattern generators, neuromechanical modelling |
| Aurelien Garivier |
Statistical Learning and Information Theory |
Lyon, France |
RL theory |
bandits, reinforcement learning theory, statistical learning, information theory |
| Benjamin Grewe |
Neural Learning and Intelligent Systems |
Zurich, Switzerland |
neuroRL |
biologically plausible learning, reinforcement learning, bio-inspired machine learning, embodied intelligence |
| Carlo D’Eramo |
Reinforcement Learning and Computational Decision-Making |
Würzburg, Germany |
RL |
reinforcement learning, computational decision-making, control tasks, multi-task reinforcement learning |
| Claire Vernade |
Foundations of Machine Learning |
Nuremberg, Germany |
RL theory |
lifelong reinforcement learning, bandits, learning theory, sequential decision making |
| Danica Kragic |
Robotics, Perception and Learning |
Stockholm, Sweden |
robotics |
robotics, perception, grasping and manipulation, planning and decision-making |
| Davide Scaramuzza |
Robotics and Perception Group |
Zurich, Switzerland |
robotics |
vision-based robotics, autonomous drones, robot perception, event cameras |
| Dieter Büchler |
Safety- and Efficiency-aligned Learning |
Linz, Austria |
RL |
reinforcement learning, soft muscular robots, robot skill learning, machine learning for control |
| Elmar Rueckert |
Chair of Cyber-Physical-Systems |
Leoben, Austria |
robotics |
robot skill learning, reinforcement learning, visual-tactile manipulation, foundation models for robotics |
| Emilie Kaufmann |
SCOOL / Sequential Decision Making |
Lille, France |
RL theory |
bandits, sequential decision making, reinforcement learning, statistical learning theory |
| Fabian Sinz |
Neuronal Intelligence Group |
Göttingen, Germany |
neuroRL |
machine learning for neural data, visual cortex models, neural system identification, biological computation |
| Florentin Wörgötter |
Computational Neurosciences |
Göttingen, Germany |
neuroRL |
temporal sequence learning, robot motor learning, sensorimotor control, neurorobotics |
| Frans Oliehoek |
Sequential Decision Making |
Delft, Netherlands |
RL theory |
multi-agent reinforcement learning, decision making under uncertainty, planning, interactive learning |
| Georg Martius |
Autonomous Learning Group |
Tübingen, Germany |
neuroRL |
model-based reinforcement learning, embodied learning, neuro-inspired learning, musculoskeletal control |
| Georgia Chalvatzaki |
Interactive Robot Perception & Learning Lab |
Darmstadt, Germany |
robotics |
robot learning, mobile manipulation, deep reinforcement learning, human-robot interaction |
| Gergely Neu |
Reinforcement Learning Theory |
Barcelona, Spain |
RL theory |
reinforcement learning theory, online optimisation, bandits, online learning |
| Gerhard Neumann |
Autonomous Learning Robots |
Karlsruhe, Germany |
RL |
reinforcement learning, imitation learning, data-efficient robot learning, human-robot interaction |
| Gianluca Baldassarre |
Laboratory of Embodied Natural and Artificial Intelligence |
Rome, Italy |
neuroRL |
embodied reinforcement learning, open-ended reinforcement learning, intrinsic motivation, developmental robotics |
| Giorgia Ramponi |
Autonomous Learning and Predictive Intelligence Lab |
Zurich, Switzerland |
RL theory |
theoretical reinforcement learning, imitation learning, multi-agent learning, sequential decision making |
| Herke van Hoof |
AMLab |
Amsterdam, Netherlands |
RL |
modular reinforcement learning, hierarchical reinforcement learning, structured prior knowledge, robot learning |
| Ilija Bogunovic |
Robust AI and Algorithmic Decision-Making |
London, United Kingdom |
RL theory |
sequential decision making, robust reinforcement learning, bandits, safe decision making |
| Jan Peters |
Intelligent Autonomous Systems |
Darmstadt, Germany |
RL |
reinforcement learning, robot learning, policy search, motor primitives |
| Jean-Baptiste Mouret |
LARSEN project-team |
Nancy, France |
robotics |
resilient robots, trial-and-error learning, quality-diversity algorithms, evolutionary robotics |
| Jens Kober |
Learning and Autonomous Control |
Delft, Netherlands |
RL |
robot learning, reinforcement learning, learning from demonstration, interactive imitation learning |
| Joni Pajarinen |
Aalto Robot Learning |
Espoo, Finland |
RL |
reinforcement learning, robotic manipulation, planning under uncertainty, multi-agent decision-making |
| Mackenzie Mathis |
Mathis Lab of Adaptive Intelligence |
Geneva, Switzerland |
neuroRL |
adaptive behaviour, sensorimotor learning, machine learning tools, neural dynamics |
| Marc Deisenroth |
Sustainability and Machine Learning Group |
London, United Kingdom |
RL |
data-efficient reinforcement learning, probabilistic modelling, autonomous decision-making, robot learning |
| Marcello Restelli |
Real-Life Reinforcement Learning Research Lab |
Milan, Italy |
RL |
reinforcement learning algorithms, multi-armed bandits, multi-agent reinforcement learning, inverse reinforcement learning |
| Marco Hutter |
Robotic Systems Lab |
Zurich, Switzerland |
robotics |
legged robotics, locomotion, mobile manipulation, field robotics |
| Matthias Bethge |
Bethge Lab |
Tübingen, Germany |
neuroRL |
machine learning, robust representation learning, visual inference, neural representations |
| Melanie Zeilinger |
Intelligent Control Systems |
Zurich, Switzerland |
RL theory |
safe reinforcement learning, learning-based control, model predictive control, control theory |
| Máté Lengyel |
Computational and Biological Learning Lab |
Cambridge, United Kingdom |
neuroRL |
learning and memory, reinforcement learning, Bayesian computation, representation learning |
| Olivier Sigaud |
ISIR |
Paris, France |
RL |
reinforcement learning, deep reinforcement learning, robot skill learning, human-machine co-learning |
| Peter Dayan |
Department for Computational Neuroscience |
Tübingen, Germany |
neuroRL |
neural reinforcement learning, decision-making, reward prediction, computational psychiatry |
| Peter Latham |
Gatsby Computational Neuroscience Unit |
London, United Kingdom |
neuroRL |
biologically plausible learning, neural computation, population coding, learning in neural systems |
| Pierre-Yves Oudeyer |
FLOWERS AI & CogSci Lab |
Bordeaux, France |
RL |
intrinsically motivated reinforcement learning, curiosity-driven learning, developmental robotics, open-ended learning |
| Robert Katzschmann |
Soft Robotics Lab |
Zurich, Switzerland |
robotics |
soft robotics, biohybrid robotics, robot design, soft actuation and sensing |
| Roberto Calandra |
Learning, Adaptive Systems and Robotics Lab |
Dresden, Germany |
robotics |
robot learning, reinforcement learning, model-based control, tactile sensing |
| Roland Siegwart |
Autonomous Systems Lab |
Zurich, Switzerland |
robotics |
autonomous robots, navigation, mapping, robot perception and planning |
| Rudolf Lioutikov |
Intuitive Robots Lab |
Karlsruhe, Germany |
RL |
learning from demonstrations, interactive learning, explainable robot learning, human-robot interaction |
| Samuele Tosatto |
Reinforcement Learning and Surroundings |
Innsbruck, Austria |
RL |
reinforcement learning, robotics, embodied learning agents, off-policy reinforcement learning |
| Sebastian Tschiatschek |
Probabilistic and Interactive Machine Learning |
Vienna, Austria |
RL theory |
reinforcement learning, exploration, interactive machine learning, probabilistic models |
| Serena Ivaldi |
HUCEBOT |
Nancy, France |
robotics |
human-centred robotics, human-robot interaction, machine learning for robots, soft robotics |
| Shimon Whiteson |
Whiteson Research Lab |
Oxford, United Kingdom |
RL |
reinforcement learning, deep reinforcement learning, imitation learning, multi-agent learning |
| Stefano Albrecht |
Autonomous Agents Research Group |
Edinburgh, United Kingdom |
RL |
multi-agent reinforcement learning, deep reinforcement learning, autonomous agents, coordination and cooperation |
| Stelian Coros |
Computational Robotics Lab |
Zurich, Switzerland |
robotics |
computational robotics, motion control, simulation, robot design |
| Wulfram Gerstner |
Laboratory of Computational Neuroscience |
Lausanne, Switzerland |
neuroRL |
reinforcement learning in the brain, spiking neural networks, synaptic plasticity, decision-making |