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Although the integration of large language models (LLMs) into robotics has unlocked transformative capabilities, it has also introduced significant safety concerns, ranging from average-case LLM errors (e.g., hallucinations) to adversarial jailbreaking attacks, which can produce harmful robot behavior in real-world settings.
K. Zhou and J. C. Doyle, Essentials of robust control . Prentice hall Upper Saddle River, NJ, 1998, vol. 104
1998
Earlier work this paper cites.
T. Wongpiromsarn, U. Topcu, N. Ozay, H. Xu, and R. M. Murray, “TuLiP: a software toolbox for receding horizon temporal logic planning,” in Proc. Int. Conf. Hybrid Syst.: Comput. Control , 2011
2011
Earlier work this paper cites.
A. Duret-Lutz, “Manipulating LTL formulas using Spot 1.0,” in Int. Symp. Autom. Technol. Verif. Anal. , 2013
2013
Earlier work this paper cites.
J. Fu, N. Atanasov, U. Topcu, and G. J. Pappas, “Optimal temporal logic planning in probabilistic semantic maps,” in IEEE Int. Conf. Robot. Autom. , 2016
2016
Earlier work this paper cites.
A. Robey, H. Hu, L. Lindemann, H. Zhang, D. V. Dimarogonas, S. Tu, and N. Matni, “Learning control barrier functions from expert demonstrations,” in IEEE Conf. Decis. Control , 2020
2020
Earlier work this paper cites.
W. H. et al., “Inner monologue: Embodied reasoning through planning with language models,” in Conf. on Robot Learning , 2022
2022
Earlier work this paper cites.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray et al. , “Training language models to follow instructions with human feedback,” Adv. Neural Inf. Process. Syst. , 2022
2022
Earlier work this paper cites.
M. Ahn, A. Brohan, N. Brown, Y. Chebotar, O. Cortes, B. David, C. Finn, C. Fu, K. Gopalakrishnan, K. Hausman et al. , “Do as I can, not as I say: Grounding language in robotic affordances,” Conf. Robot Learn. , 2022
2022
Earlier work this paper cites.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Adv. Neural Inf. Process. Syst. , 2022
2022
Earlier work this paper cites.
J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, and A. Zeng, “Code as policies: Language model programs for embodied control,” in IEEE Int. Conf. Robot. Automat. , 2023
2023
Earlier work this paper cites.
B. Chen, F. Xia, B. Ichter, K. Rao, K. Gopalakrishnan, M. S. Ryoo, A. Stone, and D. Kappler, “Open-vocabulary queryable scene representations for real world planning,” in IEEE Int. Conf. Robot. Automat. , 2023
2023
Earlier work this paper cites.
K. Rana, J. Haviland, S. Garg, J. Abou-Chakra, I. Reid, and N. Suenderhauf, “Sayplan: Grounding large language models using 3D scene graphs for scalable task planning,” in Conf. Robot Learn. , 2023
2023
Earlier work this paper cites.
D. Shah, B. Osiński, b. ichter, and S. Levine, “LM-Nav: Robotic navigation with large pre-trained models of language, vision, and action,” in Conf. Robot Learn. , 2023
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
M. Kwon, S. M. Xie, K. Bullard, and D. Sadigh, “Reward design with language models,” Int. Conf. Learn. Represent. , 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2024
Later among the works it cites.
Z. Yang, S. S. Raman, A. Shah, and S. Tellex, “Plug in the safety chip: Enforcing constraints for LLM-driven robot agents,” IEEE Int. Conf. Robot. Automat. , 2024
2024
Later among the works it cites.
J. X. Liu, A. Shah, G. Konidaris, S. Tellex, and D. Paulius, “Lang2LTL-2: Grounding spatiotemporal navigation commands using large language and vision-language models,” in Conf. Intell. Robots Syst. , 2024
2024
Later among the works it cites.
H. Huang, Z. Zhao, M. Backes, Y. Shen, and Y. Zhang, “Composite backdoor attacks against large language models,” Findings Assoc. Comput. Linguistics , 2024
2024
Later among the works it cites.
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2023
Cited alongside, same era.
B. Li, Y. Wang, J. Mao, B. Ivanovic, S. Veer, K. Leung, and M. Pavone, “Driving everywhere with large language model policy adaptation,” in IEEE Comp. Vis. Pat. Recog. , 2024
2024
Cited alongside, same era.
D. Honerkamp, M. Büchner, F. Despinoy, T. Welschehold, and A. Valada, “Language-grounded dynamic scene graphs for interactive object search with mobile manipulation,” IEEE Robot. Automat. Lett , 2024
2024
Cited alongside, same era.
J. W. Kim, T. Z. Zhao, S. Schmidgall, A. Deguet, M. Kobilarov, C. Finn, and A. Krieger, “Surgical robot transformer (SRT): Imitation learning for surgical tasks,” Conf. Robot Learn. , 2024
2024
Cited alongside, same era.
2024
Cited alongside, same era.
D. Guarascio, A. Piccirillo, and J. Reljic, “Will robots replace workers? assessing the impact of robots on employment and wages with meta-analysis,” Assessing the Impact of Robots on Employment and Wages with Meta-Analysis , 2024
2024
Cited alongside, same era.
C. Y. Kim, C. P. Lee, and B. Mutlu, “Understanding large-language model (LLM)-powered human-robot interaction,” in ACM/IEEE Int. Conf. Human-Robot Interact. , 2024
2024
Cited alongside, same era.
Y. J. Ma, W. Liang, H.-J. Wang, S. Wang, Y. Zhu, L. Fan, O. Bastani, and D. Jayaraman, “DrEureka: Language model guided sim-to-real transfer,” Robot.: Sci. Syst. , 2024
2024
Cited alongside, same era.
N. Hughes, Y. Chang, S. Hu, R. Talak, R. Abdulhai, J. Strader, and L. Carlone, “Foundations of spatial perception for robotics: Hierarchical representations and real-time systems,” Int. J. Robot. Res. , 2024
2024
Later among the works it cites.
G. R. Team, “Gemini robotics: Bringing AI into the physical world,” arXiv:2503.20020 , 2025
2025
Closest in time.
Z. Ravichandran, V. Murali, M. Tzes, G. J. Pappas, and V. Kumar, “Spine: Online semantic planning for missions with incomplete natural language specifications in unstructured environments,” IEEE Conf. Robot. Automat. , 2025
2025
Closest in time.
J. Pearce, Y. Li, F. Chen, Y. Vynnytska, D. Nie, H. Elhassan, A. Sen, T. Lee, Z. Wang, N. Gati et al. , “Scaling laws for pre-training agents and tools,” Int. Conf. Learn. Represent. , 2025
2025
Closest in time.
A. Robey, Z. Ravichandran, V. Kumar, H. Hassani, and G. J. Pappas, “Jailbreaking LLM-controlled robots,” IEEE Int. Conf. Robot. Automat. , 2025
2025
Closest in time.
K. Black, N. Brown, D. Driess, A. Esmail, M. Equi, C. Finn, N. Fusai, L. Groom, K. Hausman, B. Ichter et al. , “ π 0 \pi_{0} : A Vision-Language-Action Flow Model for General Robot Control,” Robot.: Sci. Syst. , 2025
2025
Closest in time.
B. Quartey, E. Rosen, S. Tellex, and G. Konidaris, “Verifiably following complex robot instructions with foundation models,” IEEE Int. Conf. Robot. Automat. , 2025
2025
Closest in time.
L. Brunke, Y. Zhang, R. Römer, J. Naimer, N. Staykov, S. Zhou, and A. P. Schoellig, “Semantically safe robot manipulation: From semantic scene understanding to motion safeguards,” IEEE Robot. Automat. Lett. , 2025
2025
Closest in time.
D. Yang, T. Liu, D. Zhang, A. Simoulin, X. Liu, Y. Cao, Z. Teng, X. Qian, G. Yang, J. Luo, and J. McAuley, “Code to think, think to code: A survey on code-enhanced reasoning and reasoning-driven code intelligence in LLMs,” Empirical Methods Natural Lang. Process. , 2025
2025
Closest in time.
Z. Ravichandran, I. Hounie, F. Cladera, A. Ribeiro, G. J. Pappas, and V. Kumar, “Distilling on-device language models for robot planning with minimal human intervention,” Conf. Robot Learn. , 2025
2025
Closest in time.