Kyriacos Shiarlis
Kyriacos Shiarlis
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Fast context adaptation via meta-learning
L Zintgraf, K Shiarli, V Kurin, K Hofmann, S Whiteson
International Conference on Machine Learning, 7693-7702, 2019
Inverse reinforcement learning from failure
K Shiarlis, J Messias, SA Whiteson
International Foundation for Autonomous Agents and Multiagent Systems, 2016
Varibad: A very good method for bayes-adaptive deep rl via meta-learning
L Zintgraf, K Shiarlis, M Igl, S Schulze, Y Gal, K Hofmann, S Whiteson
arXiv preprint arXiv:1910.08348, 2019
Taco: Learning task decomposition via temporal alignment for control
K Shiarlis, M Wulfmeier, S Salter, S Whiteson, I Posner
International Conference on Machine Learning, 4654-4663, 2018
Learning from demonstration in the wild
F Behbahani, K Shiarlis, X Chen, V Kurin, S Kasewa, C Stirbu, J Gomes, ...
2019 International Conference on Robotics and Automation (ICRA), 775-781, 2019
Rapidly exploring learning trees
K Shiarlis, J Messias, S Whiteson
2017 IEEE International Conference on Robotics and Automation (ICRA), 1541-1548, 2017
Teresa: A socially intelligent semi-autonomous telepresence system
K Shiarlis, J Messias, M van Someren, S Whiteson, J Kim, J Vroon, ...
Workshop on machine learning for social robotics at ICRA-2015 in Seattle, 2015
Acquiring social interaction behaviours for telepresence robots via deep learning from demonstration
K Shiarlis, J Messias, S Whiteson
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2017
Behaviour Models for Autonomous Vehicle Simulators
SA Whiteson, J Messias, X Chen, F Behbahani, K Shiarli, S Kasewa, ...
US Patent App. 16/978,446, 2021
Detaching the strings: Practical algorithms for Learning from Demonstration
KC Shiarlis
Acquiring Social Interaction Behaviours for Telepresence Robots via Deep Learning from Demonstration
S Whiteson
Robot Learning of Social Path Planning Using a Simulator
TMA Mulder, BOK Intelligentie, JTS Messias, K Shiarlis, MW van Someren
AIMS CDT Project Report: Towards One-Shot Learning From Demonstration via Reinforcement Learning
MN Finean, LM Zintgraf, K Shiarlis, V Kurin, S Whiteson
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