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ORCIDKarol Arndt
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2020 – today
- 2023
[j5]Gabriele Tiboni
, Karol Arndt, Ville Kyrki:
DROPO: Sim-to-real transfer with offline domain randomization. Robotics Auton. Syst. 166: 104432 (2023)- [j4]Oliver Struckmeier, Ievgen Redko, Anton Mallasto, Karol Arndt, Markus Heinonen, Ville Kyrki:
Learning representations that are closed-form Monge mapping optimal with application to domain adaptation. Trans. Mach. Learn. Res. 2023 (2023) - [c5]Chang Rajani, Karol Arndt, David Blanco Mulero, Kevin Sebastian Luck, Ville Kyrki:
Co-imitation: Learning Design and Behaviour by Imitation. AAAI 2023: 6200-6208 - [i12]Oliver Struckmeier, Ievgen Redko, Anton Mallasto, Karol Arndt, Markus Heinonen, Ville Kyrki:
Beyond invariant representation learning: linearly alignable latent spaces for efficient closed-form domain adaptation. CoRR abs/2305.07500 (2023) - [i11]Quentin Bouniot, Ievgen Redko, Anton Mallasto, Charlotte Laclau, Karol Arndt, Oliver Struckmeier, Markus Heinonen, Ville Kyrki, Samuel Kaski:
Understanding deep neural networks through the lens of their non-linearity. CoRR abs/2310.11439 (2023) - 2022
- [j3]Ali Ghadirzadeh, Petra Poklukar, Karol Arndt, Chelsea Finn, Ville Kyrki, Danica Kragic, Mårten Björkman:
Training and Evaluation of Deep Policies Using Reinforcement Learning and Generative Models. J. Mach. Learn. Res. 23: 174:1-174:37 (2022) - [j2]Rituraj Kaushik, Karol Arndt, Ville Kyrki:
SafeAPT: Safe Simulation-to-Real Robot Learning Using Diverse Policies Learned in Simulation. IEEE Robotics Autom. Lett. 7(3): 6838-6845 (2022) - [c4]Gabriele Tiboni, Karol Arndt, Giuseppe Averta, Ville Kyrki, Tatiana Tommasi:
Online vs. Offline Adaptive Domain Randomization Benchmark. HFR 2022: 158-173 - [i10]Gabriele Tiboni, Karol Arndt, Ville Kyrki:
DROPO: Sim-to-Real Transfer with Offline Domain Randomization. CoRR abs/2201.08434 (2022) - [i9]Rituraj Kaushik, Karol Arndt, Ville Kyrki:
SafeAPT: Safe Simulation-to-Real Robot Learning using Diverse Policies Learned in Simulation. CoRR abs/2201.13248 (2022) - [i8]Ali Ghadirzadeh, Petra Poklukar, Karol Arndt, Chelsea Finn, Ville Kyrki, Danica Kragic, Mårten Björkman:
Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models. CoRR abs/2204.08573 (2022) - [i7]Gabriele Tiboni, Karol Arndt, Giuseppe Averta, Ville Kyrki, Tatiana Tommasi:
Online vs. Offline Adaptive Domain Randomization Benchmark. CoRR abs/2206.14661 (2022) - [i6]Chang Rajani, Karol Arndt, David Blanco Mulero, Kevin Sebastian Luck, Ville Kyrki:
Co-Imitation: Learning Design and Behaviour by Imitation. CoRR abs/2209.01207 (2022) - 2021
- [j1]Karol Arndt, Ali Ghadirzadeh, Murtaza Hazara, Ville Kyrki:
Few-Shot Model-Based Adaptation in Noisy Conditions. IEEE Robotics Autom. Lett. 6(2): 4193-4200 (2021) - [c3]Karol Arndt, Oliver Struckmeier, Ville Kyrki:
Domain Curiosity: Learning Efficient Data Collection Strategies for Domain Adaptation. IROS 2021: 1259-1266 - [i5]Karol Arndt, Oliver Struckmeier, Ville Kyrki:
Domain Curiosity: Learning Efficient Data Collection Strategies for Domain Adaptation. CoRR abs/2103.07223 (2021) - [i4]Anton Mallasto, Karol Arndt, Markus Heinonen, Samuel Kaski, Ville Kyrki:
Affine Transport for Sim-to-Real Domain Adaptation. CoRR abs/2105.11739 (2021) - 2020
- [c2]Karol Arndt, Murtaza Hazara, Ali Ghadirzadeh, Ville Kyrki:
Meta Reinforcement Learning for Sim-to-real Domain Adaptation. ICRA 2020: 2725-2731 - [i3]Karol Arndt, Ali Ghadirzadeh, Murtaza Hazara, Ville Kyrki:
Few-shot model-based adaptation in noisy conditions. CoRR abs/2010.08397 (2020)
2010 – 2019
- 2019
- [c1]Aleksi Hämäläinen, Karol Arndt, Ali Ghadirzadeh, Ville Kyrki:
Affordance Learning for End-to-End Visuomotor Robot Control. IROS 2019: 1781-1788 - [i2]Aleksi Hämäläinen, Karol Arndt, Ali Ghadirzadeh, Ville Kyrki:
Affordance Learning for End-to-End Visuomotor Robot Control. CoRR abs/1903.04053 (2019) - [i1]Karol Arndt, Murtaza Hazara, Ali Ghadirzadeh, Ville Kyrki:
Meta Reinforcement Learning for Sim-to-real Domain Adaptation. CoRR abs/1909.12906 (2019)
Coauthor Index
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