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A world model creates a surrogate world to train a controller and predict safety violations by learning the internal dynamic model of systems.
J. G. Moreno-Torres, T. Raeder, R. Alaiz-Rodríguez, N. V. Chawla, and F. Herrera, “A unifying view on dataset shift in classification,” Pattern Recognition , vol. 45, no. 1, pp. 521–530, 2012. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0031320311002901
2012
Earlier work this paper cites.
X. Chen, E. Ábrahám, and S. Sankaranarayanan, “Flow*: An analyzer for non-linear hybrid systems,” in Computer Aided Verification: 25th International Conference, CAV 2013, Saint Petersburg, Russia, July 13-19, 2013. Proceedings 25 . Springer, 2013, pp. 258–263
2013
Earlier work this paper cites.
2016
Earlier work this paper cites.
D. Ha and J. Schmidhuber, “Recurrent world models facilitate policy evolution,” in Advances in Neural Information Processing Systems 31 . Curran Associates, Inc., 2018, pp. 2451–2463, https://worldmodels.github.io. [Online]. Available: https://papers.nips.cc/paper/7512-recurrent-world-models-facilitate-policy-evolution
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” 2019
2019
Earlier work this paper cites.
A. Holtzman, J. Buys, L. Du, M. Forbes, and Y. Choi, “The curious case of neural text degeneration,” 2020
2020
Earlier work this paper cites.
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” 2020
2020
Earlier work this paper cites.
H. Cui, T. Nguyen, F.-C. Chou, T.-H. Lin, J. Schneider, D. Bradley, and N. Djuric, “Deep kinematic models for kinematically feasible vehicle trajectory predictions,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) , 2020, pp. 10 563–10 569
2020
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” 2021
2021
Earlier work this paper cites.
D. Hafner, T. Lillicrap, M. Norouzi, and J. Ba, “Mastering atari with discrete world models,” 2022
2022
Cited alongside, same era.
A. Acharya, R. Russell, and N. R. Ahmed, “Competency assessment for autonomous agents using deep generative models,” 2022
2022
Cited alongside, same era.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” 2022
2022
Cited alongside, same era.
S. Wen, H. Wang, and D. Metaxas, “Social ode: Multi-agent trajectory forecasting with neural ordinary differential equations,” in European Conference on Computer Vision . Springer, 2022, pp. 217–233
2022
Cited alongside, same era.
M. Althoff, M. Forets, C. Schilling, and M. Wetzlinger, “Arch-comp22 category report: Continuous and hybrid systems with linear continuous dynamics,” in Proc. of 9th International Workshop on Applied Verification of Continuous and Hybrid Systems , 2022
S. Vemprala, R. Bonatti, A. Bucker, and A. Kapoor, “Chatgpt for robotics: Design principles and model abilities,” 2023
2023
Later among the works it cites.
A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, X. Chen, K. Choromanski, T. Ding, D. Driess, A. Dubey, C. Finn, P. Florence, C. Fu, M. G. Arenas, K. Gopalakrishnan, K. Han, K. Hausman, A. Herzog, J. Hsu, B. Ichter, A. Irpan, N. Joshi, R. Julian, D. Kalashnikov, Y. Kuang, I. Leal, L. Lee, T.-W. E. Lee, S. Levine, Y. Lu, H. Michalewski, I. Mordatch, K. Pertsch, K. Rao, K. Reymann, M. Ryoo, G. Salazar, P. Sanketi, P. Sermanet, J. Singh, A. Singh, R. Soricut, H. Tran, V. Vanhoucke, Q. Vuong, A. Wahid, S. Welker, P. Wohlhart, J. Wu, F. Xia, T. Xiao, P. Xu, S. Xu, T. Yu, and B. Zitkovich, “Rt-2: Vision-language-action models transfer web knowledge to robotic control,” 2023
2023
Later among the works it cites.
X. Huang, W. Ruan, W. Huang, G. Jin, Y. Dong, C. Wu, S. Bensalem, R. Mu, Y. Qi, X. Zhao, K. Cai, Y. Zhang, S. Wu, P. Xu, D. Wu, A. Freitas, and M. A. Mustafa, “A survey of safety and trustworthiness of large language models through the lens of verification and validation,” 2023
2023
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2022
Cited alongside, same era.
X. Qin, Y. Xia, A. Zutshi, C. Fan, and J. V. Deshmukh, “Statistical verification of cyber-physical systems using surrogate models and conformal inference,” in 2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS) . IEEE, 2022, pp. 116–126
2022
Cited alongside, same era.
V. Micheli, E. Alonso, and F. Fleuret, “Transformers are sample-efficient world models,” in The Eleventh International Conference on Learning Representations , 2023. [Online]. Available: https://openreview.net/forum?id=vhFu1Acb0xb
2023
Cited alongside, same era.
A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, P. Dollár, and R. Girshick, “Segment anything,” 2023
2023
Cited alongside, same era.
G. Team, “Gemini: A family of highly capable multimodal models,” 2023
2023
Cited alongside, same era.
M. Yang, F. Liu, Z. Chen, X. Shen, J. Hao, and J. Wang, “Causalvae: Structured causal disentanglement in variational autoencoder,” 2023
2023
Cited alongside, same era.
S. Mirchandani, F. Xia, P. Florence, B. Ichter, D. Driess, M. G. Arenas, K. Rao, D. Sadigh, and A. Zeng, “Large language models as general pattern machines,” 2023
2023
Cited alongside, same era.
Z. Jin, Y. Chen, F. Gonzalez, J. Liu, J. Zhang, J. Michael, B. Schölkopf, and M. Diab, “Role of semantic representations in an era of large language models.”
Cited in the paper.
Later among the works it cites.
S. Tan, B. Ivanovic, X. Weng, M. Pavone, and P. Kraehenbuehl, “Language conditioned traffic generation,” 2023
2023
Later among the works it cites.
A. Dixit, L. Lindemann, S. X. Wei, M. Cleaveland, G. J. Pappas, and J. W. Burdick, “Adaptive conformal prediction for motion planning among dynamic agents,” in Learning for Dynamics and Control Conference . PMLR, 2023, pp. 300–314
2023
Later among the works it cites.
Z. Mao, C. Sobolewski, and I. Ruchkin, “How safe am i given what i see? calibrated prediction of safety chances for image-controlled autonomy,” 2024
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A. Peng, I. Sucholutsky, B. Z. Li, T. R. Sumers, T. L. Griffiths, J. Andreas, and J. A. Shah, “Learning with language-guided state abstractions,” 2024
2024
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A. Bobu, A. Peng, P. Agrawal, J. A. Shah, and A. D. Dragan, “Aligning human and robot representations,” in Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction , ser. HRI ’24. ACM, Mar. 2024. [Online]. Available: http://dx.doi.org/10.1145/3610977.3634987
2024
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