| 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 8 (4), 2325-2332, 2023 | 44 | 2023 |
| Specializing versatile skill libraries using local mixture of experts O Celik, D Zhou, G Li, P Becker, G Neumann Conference on Robot Learning, 1423-1433, 2022 | 37 | 2022 |
| Evaluation of CFD predictions using different turbulence models on a film cooled guide vane under experimental conditions Q Zhang, H Xu, J Wang, G Li, L Wang, X Wu, S Ma Turbo Expo: Power for Land, Sea, and Air 56710, V05AT10A010, 2015 | 15 | 2015 |
| MaIL: Improving Imitation Learning with Mamba X Jia, Q Wang, A Donat, B Xing, G Li, H Zhou, O Celik, D Blessing, ... arXiv preprint arXiv:2406.08234, 2024 | 7 | 2024 |
| 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 The Twelfth International Conference on Learning Representations, 2024 | 6 | 2024 |
| Mp3: Movement primitive-based (re-) planning policy F Otto, H Zhou, O Celik, G Li, R Lioutikov, G Neumann arXiv preprint arXiv:2306.12729, 2023 | 6 | 2023 |
| Predicting adiabatic film effectiveness of a turbine vane by two-equation turbulence models P Prapamonthon, H Xu, J Wang, G Li Turbo Expo: Power for Land, Sea, and Air 56727, V05BT12A019, 2015 | 4 | 2015 |
| MaIL: Improving Imitation Learning with Selective State Space Models X Jia, Q Wang, A Donat, B Xing, G Li, H Zhou, O Celik, D Blessing, ... 8th Annual Conference on Robot Learning, 2024 | 2 | 2024 |
| Variational Distillation of Diffusion Policies into Mixture of Experts H Zhou, D Blessing, G Li, O Celik, X Jia, G Neumann, R Lioutikov arXiv preprint arXiv:2406.12538, 2024 | 1 | 2024 |
| An encoder-decoder architecture for smooth motion generation Z Lončarević, G Li, G Neumann, A Gams International Conference on Robotics in Alpe-Adria Danube Region, 358-366, 2023 | 1 | 2023 |
| BMP: Bridging the Gap between B-Spline and Movement Primitives W Liao, G Li, H Zhou, R Lioutikov, G Neumann arXiv preprint arXiv:2411.10336, 2024 | | 2024 |
| TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning G Li, D Tian, H Zhou, X Jiang, R Lioutikov, G Neumann arXiv preprint arXiv:2410.09536, 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 | | |
| MP3: Movement Primitive-Based (Re-) Planning Policy H Zhou, F Otto, O Celik, G Li, R Lioutikov, G Neumann CoRL 2023 Workshop on Learning Effective Abstractions for Planning (LEAP), 0 | | |
| Solving Real-World Robot Manip-ulation Tasks with Deep Rein-forcement Learning TTPR Lioutikov, H Zhou, G Li | | |
Rudolf LioutikovTT-Professor, Intuitive Robots Lab, Karlsruhe Institute of TechnologyBestätigte E-Mail-Adresse bei kit.edu