Karol Arndt
Karol Arndt
PhD Student, Aalto University
Verified email at
Cited by
Cited by
Meta reinforcement learning for sim-to-real domain adaptation
K Arndt, M Hazara, A Ghadirzadeh, V Kyrki
2020 IEEE International Conference on Robotics and Automation (ICRA), 2725-2731, 2020
Affordance learning for end-to-end visuomotor robot control
A Hämäläinen, K Arndt, A Ghadirzadeh, V Kyrki
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019
Few-shot model-based adaptation in noisy conditions
K Arndt, A Ghadirzadeh, M Hazara, V Kyrki
IEEE Robotics and Automation Letters 6 (2), 4193-4200, 2021
Domain Curiosity: Learning Efficient Data Collection Strategies for Domain Adaptation
K Arndt, O Struckmeier, V Kyrki
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021
Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models
A Ghadirzadeh, P Poklukar, K Arndt, C Finn, V Kyrki, D Kragic, ...
arXiv preprint arXiv:2204.08573, 2022
SafeAPT: Safe Simulation-to-Real Robot Learning using Diverse Policies Learned in Simulation
R Kaushik, K Arndt, V Kyrki
arXiv preprint arXiv:2201.13248, 2022
DROPO: Sim-to-Real Transfer with Offline Domain Randomization
G Tiboni, K Arndt, V Kyrki
arXiv preprint arXiv:2201.08434, 2022
Affine Transport for Sim-to-Real Domain Adaptation
A Mallasto, K Arndt, M Heinonen, S Kaski, V Kyrki
arXiv preprint arXiv:2105.11739, 2021
The system can't perform the operation now. Try again later.
Articles 1–8