| Deep black-box reinforcement learning with movement primitives F Otto, O Celik, H Zhou, H Ziesche, VA Ngo, G Neumann Conference on Robot Learning, 1244-1265, 2023 | 12 | 2023 |
| 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 | 4 | 2023 |
| HIRO: Heuristics Informed Robot Online Path Planning Using Pre-computed Deterministic Roadmaps X Huang, G Sóti, H Zhou, C Ledermann, B Hein, T Kröger 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2022 | 2 | 2022 |
| 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 | 1 | 2024 |
| 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), 2023 | | 2023 |
| 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 | | |
| Air Hockey Challenge 2023: Air-HocKIT Team Report MEBV de Bakker3Atalay, DÖE Yagmurlu, MFZJD Yang, H Zhou, X Jia, ... | | |
| Benchmarking Model-based Con-trollers for Franka Emika Robot IR Lioutikov, H Zhou | | |
| Solving Real-World Robot Manip-ulation Tasks with Deep Rein-forcement Learning TTPR Lioutikov, H Zhou, G Li | | |
Fabian OttoMicrosoft ResearchVerified email at microsoft.com
