Karol Arndt

Karol Arndt

Aalto-yliopisto

H-index: 5

Europe-Finland

About Karol Arndt

Karol Arndt, With an exceptional h-index of 5 and a recent h-index of 5 (since 2020), a distinguished researcher at Aalto-yliopisto, specializes in the field of robotics, machine learning, reinforcement learning.

His recent articles reflect a diverse array of research interests and contributions to the field:

Understanding deep neural networks through the lens of their non-linearity

DROPO: Sim-to-real transfer with offline domain randomization

Co-imitation: learning design and behaviour by imitation

Learning representations that are closed-form Monge mapping optimal with application to domain adaptation

Beyond invariant representation learning: linearly alignable latent spaces for efficient closed-form domain adaptation

Safe and efficient transfer of robot policies from simulation to the real world

Dynamic flex compensation, coordinated hoist control, and anti-sway control for load handling machines

Online vs. offline adaptive domain randomization benchmark

Karol Arndt Information

University

Position

PhD Student

Citations(all)

171

Citations(since 2020)

170

Cited By

41

hIndex(all)

5

hIndex(since 2020)

5

i10Index(all)

3

i10Index(since 2020)

3

Email

University Profile Page

Google Scholar

Karol Arndt Skills & Research Interests

robotics

machine learning

reinforcement learning

Top articles of Karol Arndt

Understanding deep neural networks through the lens of their non-linearity

arXiv preprint arXiv:2310.11439

2023/10/17

DROPO: Sim-to-real transfer with offline domain randomization

Robotics and Autonomous Systems

2023/8/1

Karol Arndt
Karol Arndt

H-Index: 3

Ville Kyrki
Ville Kyrki

H-Index: 21

Co-imitation: learning design and behaviour by imitation

Proceedings of the AAAI Conference on Artificial Intelligence

2023/6/26

Learning representations that are closed-form Monge mapping optimal with application to domain adaptation

Transactions on Machine Learning Research

2023/5/13

Beyond invariant representation learning: linearly alignable latent spaces for efficient closed-form domain adaptation

arXiv preprint arXiv:2305.07500

2023/5/12

Safe and efficient transfer of robot policies from simulation to the real world

2023

Karol Arndt
Karol Arndt

H-Index: 3

Dynamic flex compensation, coordinated hoist control, and anti-sway control for load handling machines

2022/12/29

Online vs. offline adaptive domain randomization benchmark

2022/9/22

Safeapt: Safe simulation-to-real robot learning using diverse policies learned in simulation

IEEE Robotics and Automation Letters

2022/5/23

Training and evaluation of deep policies using reinforcement learning and generative models

arXiv preprint arXiv:2204.08573

2022/4/18

Domain curiosity: Learning efficient data collection strategies for domain adaptation

2021/9/27

Affine transport for sim-to-real domain adaptation

arXiv preprint arXiv:2105.11739

2021/5/25

Few-shot model-based adaptation in noisy conditions

IEEE Robotics and Automation Letters

2021/3/23

Meta reinforcement learning for sim-to-real domain adaptation

2020/5/31

See List of Professors in Karol Arndt University(Aalto-yliopisto)

Co-Authors

academic-engine