| Reward (mis) design for autonomous driving WB Knox, A Allievi, H Banzhaf, F Schmitt, P Stone Artificial Intelligence 316, 103829, 2023 | 122 | 2023 |
| Inverse reinforcement learning with simultaneous estimation of rewards and dynamics M Herman, T Gindele, J Wagner, F Schmitt, W Burgard Artificial intelligence and statistics, 102-110, 2016 | 86 | 2016 |
| Hierarchies of planning and reinforcement learning for robot navigation J Wöhlke, F Schmitt, H van Hoof 2021 IEEE international conference on robotics and automation (ICRA), 10682 …, 2021 | 30 | 2021 |
| I see what you see: Inferring sensor and policy models of human real-world motor behavior F Schmitt, HJ Bieg, M Herman, C Rothkopf Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 24 | 2017 |
| A performance-based start state curriculum framework for reinforcement learning J Wöhlke, F Schmitt, H van Hoof Proceedings of the 19th International Conference on Autonomous Agents and …, 2020 | 23 | 2020 |
| Predicting lane keeping behavior of visually distracted drivers using inverse suboptimal control F Schmitt, HJ Bieg, D Manstetten, M Herman, R Stiefelhagen 2016 IEEE Intelligent Vehicles Symposium (IV), 412-418, 2016 | 15 | 2016 |
| Exact maximum entropy inverse optimal control for modeling human attention switching and control F Schmitt, HJ Bieg, D Manstetten, M Herman, R Stiefelhagen 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2016 | 9 | 2016 |
| Learning high-level navigation strategies via inverse reinforcement learning: A comparative analysis M Herman, T Gindele, J Wagner, F Schmitt, C Quignon, W Burgard AI 2016: Advances in Artificial Intelligence: 29th Australasian Joint …, 2016 | 6 | 2016 |
| Predicting strategies of driving in presence of additional visually demanding tasks: inverse optimal control estimation of steering and glance behaviour models F Schmitt, A Korthauer, D Manstetten, HJ Bieg UR: BAN Human Factors in Traffic: Approaches for Safe, Efficient and Stress …, 2018 | 3 | 2018 |
| Value refinement network (VRN) J Wöhlke, F Schmitt, H van Hoof | 2 | 2022 |
| Simultaneous estimation of rewards and dynamics from noisy expert demonstrations. M Herman, T Gindele, J Wagner, F Schmitt, W Burgard ESANN, 2016 | 2 | 2016 |
| Learning Hierarchical Planning-Based Policies from Offline Data J Wöhlke, F Schmitt, H van Hoof Joint European Conference on Machine Learning and Knowledge Discovery in …, 2023 | 1 | 2023 |
| Method for Evaluating a Traffic Scene with Several Road Users B Coors, F Schmitt, J Goth, M Naumann, R Cimurs US Patent App. 18/631,899, 2024 | | 2024 |
| Training an autoencoder to obtain a generative model F Schmitt US Patent App. 18/508,078, 2024 | | 2024 |
| Reward (mis) design for autonomous driving (abstract reprint) WB Knox, A Allievi, H Banzhaf, F Schmitt, P Stone Proceedings of the AAAI Conference on Artificial Intelligence 38 (20), 22702 …, 2024 | | 2024 |
| Device and method for controlling a robot JG Woehlke, F Schmitt, H Van Hoof US Patent 11,934,176, 2024 | | 2024 |
| Selection of Driving Maneuvers for at Least Semi-Autonomously Driving Vehicles F Schmitt, M Stoll, J Goth, HA Banzhaf, JM Doellinger, M Hanselmann US Patent App. 18/255,849, 2024 | | 2024 |
| Method for controlling an agent F Schmitt US Patent App. 18/045,382, 2023 | | 2023 |
| Device and method for controlling an agent JG Woehlke, F Schmitt, H Van Hoof US Patent App. 17/898,846, 2023 | | 2023 |
| Method and device for activating a technical unit JG Woehlke, F Schmitt US Patent App. 17/601,366, 2022 | | 2022 |
Michael HermanBosch Center for Artificial IntelligenceEmail verificata su informatik.uni-freiburg.de