| On the opportunities and risks of foundation models R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021 | 4207 | 2021 |
| Planning for autonomous cars that leverage effects on human actions. D Sadigh, S Sastry, SA Seshia, AD Dragan Robotics: Science and systems 2, 1-9, 2016 | 628 | 2016 |
| Active preference-based learning of reward functions D Sadigh, A Dragan, S Sastry, S Seshia | 420 | 2017 |
| Toward verified artificial intelligence SA Seshia, D Sadigh, SS Sastry Communications of the ACM 65 (7), 46-55, 2022 | 408 | 2022 |
| Open problems and fundamental limitations of reinforcement learning from human feedback S Casper, X Davies, C Shi, TK Gilbert, J Scheurer, J Rando, R Freedman, ... arXiv preprint arXiv:2307.15217, 2023 | 375 | 2023 |
| Reactive synthesis from signal temporal logic specifications V Raman, A Donzé, D Sadigh, RM Murray, SA Seshia Proceedings of the 18th international conference on hybrid systems …, 2015 | 352 | 2015 |
| Hierarchical game-theoretic planning for autonomous vehicles JF Fisac, E Bronstein, E Stefansson, D Sadigh, SS Sastry, AD Dragan 2019 International conference on robotics and automation (ICRA), 9590-9596, 2019 | 295 | 2019 |
| Open x-embodiment: Robotic learning datasets and rt-x models A O'Neill, A Rehman, A Gupta, A Maddukuri, A Gupta, A Padalkar, A Lee, ... arXiv preprint arXiv:2310.08864, 2023 | 284 | 2023 |
| Multi-agent generative adversarial imitation learning J Song, H Ren, D Sadigh, S Ermon Advances in neural information processing systems 31, 2018 | 279 | 2018 |
| Information gathering actions over human internal state D Sadigh, SS Sastry, SA Seshia, A Dragan 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2016 | 243 | 2016 |
| Planning for cars that coordinate with people: leveraging effects on human actions for planning and active information gathering over human internal state D Sadigh, N Landolfi, SS Sastry, SA Seshia, AD Dragan Autonomous Robots 42, 1405-1426, 2018 | 228 | 2018 |
| Language to rewards for robotic skill synthesis W Yu, N Gileadi, C Fu, S Kirmani, KH Lee, MG Arenas, HTL Chiang, ... arXiv preprint arXiv:2306.08647, 2023 | 226 | 2023 |
| A learning based approach to control synthesis of markov decision processes for linear temporal logic specifications D Sadigh, ES Kim, S Coogan, SS Sastry, SA Seshia 53rd IEEE Conference on Decision and Control, 1091-1096, 2014 | 214 | 2014 |
| Reward design with language models M Kwon, SM Xie, K Bullard, D Sadigh arXiv preprint arXiv:2303.00001, 2023 | 194 | 2023 |
| Safe control under uncertainty with probabilistic signal temporal logic D Sadigh, A Kapoor Proceedings of Robotics: Science and Systems XII, 2016 | 172 | 2016 |
| Robots that ask for help: Uncertainty alignment for large language model planners AZ Ren, A Dixit, A Bodrova, S Singh, S Tu, N Brown, P Xu, L Takayama, ... arXiv preprint arXiv:2307.01928, 2023 | 168 | 2023 |
| Synthesis for human-in-the-loop control systems W Li, D Sadigh, SS Sastry, SA Seshia Tools and Algorithms for the Construction and Analysis of Systems: 20th …, 2014 | 160 | 2014 |
| Learning reward functions by integrating human demonstrations and preferences M Palan, NC Landolfi, G Shevchuk, D Sadigh arXiv preprint arXiv:1906.08928, 2019 | 156 | 2019 |
| Large language models as general pattern machines S Mirchandani, F Xia, P Florence, B Ichter, D Driess, MG Arenas, K Rao, ... arXiv preprint arXiv:2307.04721, 2023 | 148 | 2023 |
| Will UML 2.0 be agile or awkward? C Kobryn Communications of the ACM 45 (1), 107-110, 2002 | 147* | 2002 |
Sanjit A. SeshiaProfessor of EECS, University of California, BerkeleyGeverifieerd e-mailadres voor eecs.berkeley.edu