| ProDMP: A Unified Perspective on Dynamic and Probabilistic Movement Primitives G Li, Z Jin, M Volpp, F Otto, R Lioutikov, G Neumann IEEE Robotics and Automation Letters (RA-L) 8 (4), 2325-2332, 2023 | 49 | 2023 |
| Differentiable Trust Region Layers for Deep Reinforcement Learning F Otto, P Becker, NA Vien, HC Ziesche, G Neumann International Conference for Learning Representations (ICLR), 2021 | 33 | 2021 |
| Reinforcement learning algorithms: analysis and applications B Belousov, H Abdulsamad, P Klink, S Parisi, J Peters Springer, 2021 | 31 | 2021 |
| Deep Black-Box Reinforcement Learning with Movement Primitives F Otto, O Celik, H Zhou, H Ziesche, NA Vien, G Neumann 6th Annual Conference on Robot Learning (CoRL), 2022 | 25 | 2022 |
| Model-Free Deep Reinforcement Learning — Algorithms and Applications F Otto Reinforcement Learning Algorithms: Analysis and Applications, 109-121, 2021 | 14 | 2021 |
| Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning G Li, H Zhou, D Roth, S Thilges, F Otto, R Lioutikov, G Neumann arXiv preprint arXiv:2401.11437, 2024 | 7 | 2024 |
| MP3: Movement Primitive-Based (Re-) Planning Policy F Otto, H Zhou, O Celik, G Li, R Lioutikov, G Neumann CoRL 2023 Workshop on Learning Effective Abstractions for Planning (LEAP), 2023 | 6 | 2023 |
| Reinforcement learning from multiple sensors via joint representations P Becker, S Markgraf, F Otto, G Neumann arXiv preprint arXiv:2302.05342, 2023 | 3 | 2023 |
| Combining Reconstruction and Contrastive Methods for Multimodal Representations in RL P Becker, S Mossburger, F Otto, G Neumann Reinforcement Learning Conference, 2024 | 2* | 2024 |
| Air Hockey Challenge 2023: Air-HocKIT Team Report M de Bakker3Atalay, DOE Yagmurlu, Y MFZJD, H Zhou, X Jia, O Celik, ... Air Hockey Challenge at Advances in neural information processing systems, 2023 | 1 | 2023 |
| Stable Optimization of Gaussian Likelihoods D Megerle, F Otto, M Volpp, G Neumann | 1 | 2023 |
| Device and method for controlling a robotic device F Otto US Patent App. 17/447,553, 2022 | 1 | 2022 |
| Differentiable Trust Region Projection Layers F Otto Universität Tübingen, 2024 | | 2024 |
| Vlearn: Off-Policy Learning with Efficient State-Value Function Estimation F Otto, P Becker, NA Vien, G Neumann arXiv preprint arXiv:2403.04453, 2024 | | 2024 |
| Method for controlling a technical system F Otto, G Neumann US Patent App. 18/356,088, 2024 | | 2024 |
| Method for training a control policy for controlling a technical system F Otto, G Neumann, AV Ngo, H Ziesche US Patent App. 18/360,071, 2024 | | 2024 |
| Latent Space Exploration and Trajectory Space Update in Temporally-Correlated Episodic Reinforcement Learning G Li, H Zhou, D Roth, S Thilges, F Otto, R Lioutikov, G Neumann ICRA 2024 Workshop {\textemdash} Back to the Future: Robot Learning Going …, 0 | | |
Gerhard NeumannProfessor, Karlsruhe Institute of Technology (KIT)Verified email at robot-learning.de
