Fabian Otto

According to our database, Fabian Otto authored at least 10 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Vlearn: Off-Policy Learning with Efficient State-Value Function Estimation.

Fabian Otto, Philipp Becker, Ngo Anh Vien, Gerhard Neumann

CoRR, 2024

Combining Reconstruction and Contrastive Methods for Multimodal Representations in RL.

Philipp Becker, Sebastian Mossburger, Fabian Otto, Gerhard Neumann

RLJ, 2024

Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning.

Ge Li, Hongyi Zhou, Dominik Roth, Serge Thilges, Fabian Otto, Rudolf Lioutikov, Gerhard Neumann

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

ProDMP: A Unified Perspective on Dynamic and Probabilistic Movement Primitives.

Ge Li, Zeqi Jin, Michael Volpp, Fabian Otto, Rudolf Lioutikov, Gerhard Neumann

IEEE Robotics Autom. Lett., April, 2023

MP3: Movement Primitive-Based (Re-)Planning Policy.

Fabian Otto, Hongyi Zhou, Onur Celik, Ge Li, Rudolf Lioutikov, Gerhard Neumann

CoRR, 2023

Reinforcement Learning from Multiple Sensors via Joint Representations.

Philipp Becker, Sebastian Markgraf, Fabian Otto, Gerhard Neumann

CoRR, 2023

Attitudes towards Client-Side Scanning for CSAM, Terrorism, Drug Trafficking, Drug Use and Tax Evasion in Germany.

Lisa Geierhaas, Fabian Otto, Maximilian Häring, Matthew Smith

Proceedings of the 44th IEEE Symposium on Security and Privacy, 2023

2022

ProDMPs: A Unified Perspective on Dynamic and Probabilistic Movement Primitives.

Ge Li, Zeqi Jin, Michael Volpp, Fabian Otto, Rudolf Lioutikov, Gerhard Neumann

CoRR, 2022

Deep Black-Box Reinforcement Learning with Movement Primitives.

Fabian Otto, Onur Celik, Hongyi Zhou, Hanna Ziesche, Ngo Anh Vien, Gerhard Neumann

Proceedings of the Conference on Robot Learning, 2022

2021

Differentiable Trust Region Layers for Deep Reinforcement Learning.

Fabian Otto, Philipp Becker, Ngo Anh Vien, Hanna Carolin Maria Ziesche, Gerhard Neumann

Proceedings of the 9th International Conference on Learning Representations, 2021