[18] Y. Inoue and H. Ohashi. Prompter: Utilizing large language model prompting for a data efficient embodied instruction following. arXiv preprint arXiv:2211.03267, 2022.
[19] M. Johnson, K. Hofmann, T. Hutton, and D. Bignell. The malmo platform for artificial intelligence experimentation. In *Ijcai*, pages 4246–4247. Citeseer, 2016.
[20] E. Kolve, R. Mottaghi, W. Han, E. VanderBilt, L. Weihs, A. Herrasti, D. Gordon, Y. Zhu, A. Gupta, and A. Farhadi. Ai2-thor: An interactive 3d environment for visual ai. arXiv preprint arXiv:1712.05474, 2017.
[21] A. Mandlekar, Y. Zhu, A. Garg, J. Booher, M. Spero, A. Tung, J. Gao, J. Emmons, A. Gupta, E. Orbay, et al. Roboturk: A crowdsourcing platform for robotic skill learning through imitation. In Conference on Robot Learning, pages 879-893. PMLR, 2018.
[22] S. Y. Min, D. S. Chaplot, P. K. Ravikumar, Y. Bisk, and R. Salakhutdinov. FILM: Following instructions in language with modular methods. In International Conference on Learning Representations, 2022.
[23] D. Misra, A. Bennett, V. Blukis, E. Niklasson, M. Shatkhin, and Y. Artzi. Mapping instructions to actions in 3d environments with visual goal prediction. arXiv preprint arXiv:1809.00786, 2018.
[24] K. Nguyen and H. Daumé III. Help, anna! visual navigation with natural multimodal assistance via retrospective curiosity-encouraging imitation learning. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), November 2019.
[25] U. of Michigan. Seagull: An embodied agent for instruction following through situated dialog. In Alexa Prize SimBot Challenge Proceedings, 2023.
[26] A. Padmakumar, J. Thomason, A. Shrivastava, P. Lange, A. Narayan-Chen, S. Gella, R. Pi-ramithu, G. Tur, and D. Hakkani-Tur. TEACH: Task-driven Embodied Agents that Chat. In Conference on Artificial Intelligence (AAAI), 2022.
[27] A. Pashevich, C. Schmid, and C. Sun. Episodic transformer for vision-and-language navigation. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 15922-15932, 2021.
[28] 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, pages 8494-8502, 2018.
[29] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021.
[30] A. Ram, S. Ontañón, and M. Mehta. Artificial intelligence for adaptive computer games. In *The Florida AI Research Society*, 2007.
[31] H. R. Roman, Y. Bisk, J. Thomason, A. Celikyilmaz, and J. Gao. Rmm: A recursive mental model for dialog navigation. In Findings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020.
[32] M. Savva, A. Kadian, O. Maksymets, Y. Zhao, E. Wijmans, B. Jain, J. Straub, J. Liu, V. Koltun, J. Malik, et al. Habitat: A platform for embodied ai research. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9339–9347, 2019.
[33] M. Shridhar, J. Thomason, D. Gordon, Y. Bisk, W. Han, R. Mottaghi, L. Zettlemoyer, and D. Fox. Alfred: A benchmark for interpreting grounded instructions for everyday tasks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10740-10749, 2020.
[34] 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. arXiv preprint arXiv:2209.11302, 2022.