LLM-State: Open World State Representation for Long-horizon Task Planning with Large Language Model

R. E. Fikes and N. J. Nilsson, “Strips: A new approach to the application of theorem proving to problem solving,” Artificial intelligence , vol. 2, no. 3-4, pp. 189–208, 1971

1971

Earlier work this paper cites.

M. Fox and D. Long, “Pddl2. 1: An extension to pddl for expressing temporal planning domains,” Journal of artificial intelligence research , vol. 20, pp. 61–124, 2003

2003

Earlier work this paper cites.

M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, A. Y. Ng et al. , “Ros: an open-source robot operating system,” in ICRA workshop on open source software , vol. 3, no. 3.2. Kobe, Japan, 2009, p. 5

2009

Earlier work this paper cites.

G. Brewka, T. Eiter, and M. Truszczyński, “Answer set programming at a glance,” Communications of the ACM , vol. 54, no. 12, pp. 92–103, 2011

2011

Earlier work this paper cites.

S. Ross, G. Gordon, and D. Bagnell, “A reduction of imitation learning and structured prediction to no-regret online learning,” in Proceedings of the fourteenth international conference on artificial intelligence and statistics . JMLR Workshop and Conference Proceedings, 2011, pp. 627–635

2011

Earlier work this paper cites.

M. Wise, M. Ferguson, D. King, E. Diehr, and D. Dymesich, “Fetch and freight: Standard platforms for service robot applications,” in Workshop on autonomous mobile service robots , 2016, pp. 1–6

2016

Earlier work this paper cites.

X. Puig, K. Ra, M. Boben, J. Li, T. Wang, S. Fidler, and A. Torralba, “Virtualhome: Simulating household activities via programs,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 8494–8502

2018

Earlier work this paper cites.

W. Huang, P. Abbeel, D. Pathak, and I. Mordatch, “Language models as zero-shot planners: Extracting actionable knowledge for embodied agents,” in International Conference on Machine Learning . PMLR, 2022, pp. 9118–9147

2022

Earlier work this paper cites.

K. Valmeekam, A. Olmo, S. Sreedharan, and S. Kambhampati, “Large language models still can’t plan (a benchmark for llms on planning and reasoning about change),” in NeurIPS 2022 Foundation Models for Decision Making Workshop , 2022

2022

Earlier work this paper cites.

T. Silver, V. Hariprasad, R. S. Shuttleworth, N. Kumar, T. Lozano-Pérez, and L. P. Kaelbling, “Pddl planning with pretrained large language models,” in NeurIPS 2022 Foundation Models for Decision Making Workshop , 2022

2022

Earlier work this paper cites.

A. Farid, S. Veer, and A. Majumdar, “Task-driven out-of-distribution detection with statistical guarantees for robot learning,” in Conference on Robot Learning . PMLR, 2022, pp. 970–980

2022

Earlier work this paper cites.

S. Vemprala, R. Bonatti, A. Bucker, and A. Kapoor, “Chatgpt for robotics: Design principles and model abilities,” Microsoft Auton. Syst. Robot. Res , vol. 2, p. 20, 2023

2023

Earlier work this paper cites.

OpenAI, “Gpt-4 technical report,” 2023

2023

Earlier work this paper cites.

W. Huang, F. Xia, T. Xiao, H. Chan, J. Liang, P. Florence, A. Zeng, J. Tompson, I. Mordatch, Y. Chebotar et al. , “Inner monologue: Embodied reasoning through planning with language models,” in Conference on Robot Learning . PMLR, 2023, pp. 1769–1782

2023

Cited alongside, same era.

I. Singh, V. Blukis, A. Mousavian, A. Goyal, D. Xu, J. Tremblay, D. Fox, J. Thomason, and A. Garg, “Progprompt: Generating situated robot task plans using large language models,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 11 523–11 530

2023

Cited alongside, same era.

C. H. Song, J. Wu, C. Washington, B. M. Sadler, W.-L. Chao, and Y. Su, “Llm-planner: Few-shot grounded planning for embodied agents with large language models,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2023

2023

Cited alongside, same era.

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,” arXiv preprint arXiv:2307.06135 , 2023

B. Zhang and H. Soh, “Large language models as zero-shot human models for human-robot interaction,” arXiv preprint arXiv:2303.03548 , 2023

Original

2023

Closest in time.

Y. Ding, X. Zhang, C. Paxton, and S. Zhang, “Task and motion planning with large language models for object rearrangement,” arXiv preprint arXiv:2303.06247 , 2023

Original

2023

Closest in time.

Y. Ding, X. Zhang, S. Amiri, N. Cao, H. Yang, A. Kaminski, C. Esselink, and S. Zhang, “Integrating action knowledge and llms for task planning and situation handling in open worlds,” arXiv preprint arXiv:2305.17590 , 2023

Original

2023

Closest in time.

Z. Zhao, W. S. Lee, and D. Hsu, “Large language models as commonsense knowledge for large-scale task planning,” arXiv preprint arXiv:2305.14078 , 2023

Original

2023

Closest in time.