| A review of safe reinforcement learning: Methods, theory and applications S Gu, L Yang, Y Du, G Chen, F Walter, J Wang, A Knoll arXiv preprint arXiv:2205.10330, 2022 | 229 | 2022 |
| The review unmanned surface vehicle path planning: based on multi-modality constraint C Zhou, S Gu, Y Wen, Z Du, C Xiao, L Huang, M Zhu Ocean Engineering 200, 107043, 2020 | 144 | 2020 |
| Safe multi-agent reinforcement learning for multi-robot control S Gu, JG Kuba, Y Chen, Y Du, L Yang, A Knoll, Y Yang Artificial Intelligence 319, 103905, 2023 | 86* | 2023 |
| Settling the variance of multi-agent policy gradients JG Kuba, M Wen, L Meng, S Gu, H Zhang, DH Mguni, J Wang, Y Yang NeurIPS 2021, 2021 | 52 | 2021 |
| Multiobjective scheduling strategy with genetic algorithm and time-enhanced A* planning for autonomous parking robotics in high-density unmanned parking lots G Chen, J Hou, J Dong, Z Li, S Gu, B Zhang, J Yu, A Knoll IEEE/ASME Transactions on Mechatronics 26 (3), 1547-1557, 2020 | 39 | 2020 |
| Constrained reinforcement learning for vehicle motion planning with topological reachability analysis S Gu, G Chen, L Zhang, J Hou, Y Hu, A Knoll Robotics 11 (4), 81, 2022 | 23 | 2022 |
| Motion planning for an unmanned surface vehicle based on topological position maps C Zhou, S Gu, Y Wen, Z Du, C Xiao, L Huang, M Zhu Ocean Engineering 198, 106798, 2020 | 22 | 2020 |
| A motion planning method for unmanned surface vehicle in restricted waters S Gu, C Zhou, Y Wen, X Zhong, M Zhu, C Xiao, Z Du Proceedings of the Institution of Mechanical Engineers, Part M: Journal of …, 2020 | 21 | 2020 |
| KAM-Net: Keypoint-aware and keypoint-matching network for vehicle detection from 2-D point cloud T Zou, G Chen, Z Li, W He, S Qu, S Gu, A Knoll IEEE Transactions on Artificial Intelligence 3 (2), 207-217, 2021 | 17 | 2021 |
| A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors S Gu, A Kshirsagar, Y Du, G Chen, J Peters, A Knoll Frontiers in neurorobotics 17, 2023 | 12 | 2023 |
| Pole-based localization for autonomous vehicles in urban scenarios using local grid map-based method F Lu, G Chen, J Dong, X Yuan, S Gu, A Knoll 2020 5th International Conference on Advanced Robotics and Mechatronics …, 2020 | 10 | 2020 |
| Path search of unmanned surface vehicle based on topological location S Gu, C Zhou, Y Wen, C Xiao, Z Du, L Huang Navig. China 42 (2), 52-58, 2019 | 10* | 2019 |
| A Circle Grid-based Approach for Obstacle Avoidance Motion Planning of Unmanned Surface Vehicles M Zhu, C Xiao, S Gu, Z Du, Y Wen Proceedings of the Institution of Mechanical Engineers, Part M: Journal of …, 2022 | 9 | 2022 |
| Motion planning for an unmanned surface vehicle with wind and current effects S Gu, C Zhou, Y Wen, C Xiao, A Knoll Journal of Marine Science and Engineering 10 (3), 420, 2022 | 7 | 2022 |
| Uac: Offline reinforcement learning with uncertain action constraint J Guan, S Gu, Z Li, J Hou, Y Yang, G Chen, C Jiang IEEE Transactions on Cognitive and Developmental Systems 16 (2), 671-680, 2023 | 6 | 2023 |
| Computing position error margin for a USV due to wind and current with a trajectory model S Gu, M Zhu, G Chen, Y Wen, A Knoll Ocean Engineering 262, 111950, 2022 | 6 | 2022 |
| Safe Multiagent Learning With Soft Constrained Policy Optimization in Real Robot Control S Gu, D Huang, M Wen, G Chen, A Knoll IEEE Transactions on Industrial Informatics, 2024 | 4 | 2024 |
| Hybrid Residual Multiexpert Reinforcement Learning for Spatial Scheduling of High-Density Parking Lots J Hou, G Chen, Z Li, W He, S Gu, A Knoll, C Jiang IEEE transactions on cybernetics, 2023 | 4 | 2023 |
| Unidirectional bend drive chain and lift mechanism and boom barrier including same M Dong, C Zhang, Q Shi, Y Chen, SUN Zhibin, F Tang, S Li, S Gu US Patent 9,945,083, 2018 | 3 | 2018 |
| Balance reward and safety optimization for safe reinforcement learning: A perspective of gradient manipulation S Gu, B Sel, Y Ding, L Wang, Q Lin, M Jin, A Knoll Proceedings of the AAAI Conference on Artificial Intelligence 38 (19), 21099 …, 2024 | 2 | 2024 |
Alois KnollTechnische Universität München在 in.tum.de 的电子邮件经过验证
