Socratic Planner: Self-QA-Based Zero-Shot Planning for Embodied Instruction Following

Liu, H., Liu, Y., He, H., Yang, H.: Lebp–language expectation & binding policy: A two-stream framework for embodied vision-and-language interaction task learning agents. arXiv preprint arXiv:2203.04637 (2022)

Original

2022

Cited alongside, same era.

Min, S.Y., Chaplot, D.S., Ravikumar, P.K., Bisk, Y., Salakhutdinov, R.: FILM: Following instructions in language with modular methods. In: International Conference on Learning Representations (2022)

2022

Cited alongside, same era.

Murray, M., Cakmak, M.: Following natural language instructions for household tasks with landmark guided search and reinforced pose adjustment. IEEE Robotics and Automation Letters 7

2022

Cited alongside, same era.

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35

2022

Cited alongside, same era.

Press, O., Zhang, M., Min, S., Schmidt, L., Smith, N.A., Lewis, M.: Measuring and narrowing the compositionality gap in language models. arXiv preprint arXiv:2210.03350 (2022)

Original

2022

Cited alongside, same era.

Raman, S.S., Cohen, V., Rosen, E., Idrees, I., Paulius, D., Tellex, S.: Planning with large language models via corrective re-prompting. In: NeurIPS 2022 Foundation Models for Decision Making Workshop (2022), https://openreview.net/forum?id=cMDMRBe1TKs

2022

Cited alongside, same era.

Song, C.H., Kil, J., Pan, T.Y., Sadler, B.M., Chao, W.L., Su, Y.: One step at a time: Long-horizon vision-and-language navigation with milestones. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 15482–15491 (2022)

2022

Cited alongside, same era.

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q.V., Zhou, D., et al.: Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems 35

2022

Cited alongside, same era.

Lu, Y., Feng, W., Zhu, W., Xu, W., Wang, X.E., Eckstein, M., Wang, W.Y.: Neuro-symbolic procedural planning with commonsense prompting. In: The Eleventh International Conference on Learning Representations (2023)

2023

Later among the works it cites.

Shridhar, K., Stolfo, A., Sachan, M.: Distilling reasoning capabilities into smaller language models. In: Findings of the Association for Computational Linguistics: ACL 2023. pp. 7059–7073 (2023)

2023

Later among the works it cites.

Singh, I., Blukis, V., Mousavian, A., Goyal, A., Xu, D., Tremblay, J., Fox, D., Thomason, J., Garg, A.: Progprompt: Generating situated robot task plans using large language models. In: 2023 IEEE International Conference on Robotics and Automation (ICRA). pp. 11523–11530. IEEE (2023)

2023

Later among the works it cites.

Song, C.H., Wu, J., Washington, C., Sadler, B.M., Chao, W.L., Su, Y.: 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) (2023)

2023

Later among the works it cites.

Wang, X., Wei, J., Schuurmans, D., Le, Q.V., Chi, E.H., Narang, S., Chowdhery, A., Zhou, D.: Self-consistency improves chain of thought reasoning in language models. In: The Eleventh International Conference on Learning Representations (2023)

2023

Later among the works it cites.

Wu, Z., Wang, Z., Xu, X., Lu, J., Yan, H.: Embodied task planning with large language models. arXiv preprint arXiv:2307.01848 (2023)

Original

2023

Later among the works it cites.

Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K.R., Cao, Y.: React: Synergizing reasoning and acting in language models. In: The Eleventh International Conference on Learning Representations (2023)

2023

Later among the works it cites.

Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q.V., Chi, E.H.: Least-to-most prompting enables complex reasoning in large language models. In: The Eleventh International Conference on Learning Representations (2023)

2023

Later among the works it cites.

Choi, J.W., Yoon, Y., Ong, H., Kim, J., Jang, M.: Lota-bench: Benchmarking language-oriented task planners for embodied agents. In: The Twelfth International Conference on Learning Representations (2024)

2024

Closest in time.