Fetching the paper…
Reading the bibliography…
In the realm of data-driven AI technology, the application of open-source large language models (LLMs) in robotic task planning represents a significant milestone.
Puig, X., Ra, K., Boben, M., Li, J., Wang, T., Fidler, S., Torralba, A.: Virtualhome: Simulating household activities via programs. In: CVPR. pp. 8494–8502. Computer Vision Foundation / IEEE Computer Society (2018)
2018
Earlier work this paper cites.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI blog 1
2019
Earlier work this paper cites.
Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. In: NeurIPS (2020)
2020
Earlier work this paper cites.
Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., Stoyanov, V.: Unsupervised cross-lingual representation learning at scale. In: ACL. pp. 8440–8451. Association for Computational Linguistics (2020)
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Du, Z., Qian, Y., Liu, X., Ding, M., Qiu, J., Yang, Z., Tang, J.: GLM: general language model pretraining with autoregressive blank infilling. In: ACL (1). pp. 320–335. Association for Computational Linguistics (2022)
2022
Earlier work this paper cites.
Fiorini, L., Sorrentino, A., Pistolesi, M., Becchimanzi, C., Tosi, F., Cavallo, F.: Living with a telepresence robot: Results from a field-trial. IEEE Robotics Autom. Lett. 7
2022
Earlier work this paper cites.
Guhur, P., Chen, S., Pinel, R.G., Tapaswi, M., Laptev, I., Schmid, C.: Instruction-driven history-aware policies for robotic manipulations. In: CoRL. Proceedings of Machine Learning Research, vol. 205, pp. 175–187. PMLR (2022)
2022
Earlier work this paper cites.
Huang, W., Abbeel, P., Pathak, D., Mordatch, I.: Language models as zero-shot planners: Extracting actionable knowledge for embodied agents. In: ICML. Proceedings of Machine Learning Research, vol. 162, pp. 9118–9147. PMLR (2022)
2022
Earlier work this paper cites.
Huang, W., Xia, F., Xiao, T., Chan, H., Liang, J., Florence, P., Zeng, A., Tompson, J., Mordatch, I., Chebotar, Y., et al.: Inner monologue: Embodied reasoning through planning with language models. In: CoRL. Proceedings of Machine Learning Research, vol. 205, pp. 1769–1782. PMLR (2022)
2022
Earlier work this paper cites.
Ichter, B., Brohan, A., Chebotar, Y., Finn, C., Hausman, K., Herzog, A., Ho, D., Ibarz, J., Irpan, A., Jang, E., et al.: Do as I can, not as I say: Grounding language in robotic affordances. In: CoRL. Proceedings of Machine Learning Research, vol. 205, pp. 287–318. PMLR (2022)
2022
Earlier work this paper cites.
Li, S., Puig, X., Paxton, C., Du, Y., Wang, C., Fan, L., Chen, T., Huang, D., Akyürek, E., Anandkumar, A., et al.: Pre-trained language models for interactive decision-making. In: NeurIPS (2022)
2022
Earlier work this paper cites.
Liu, H., Tam, D., Muqeeth, M., Mohta, J., Huang, T., Bansal, M., Raffel, C.: Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning. In: NeurIPS (2022)
2022
Earlier work this paper cites.
2022
Cited alongside, same era.
Wei, J., Bosma, M., Zhao, V.Y., Guu, K., Yu, A.W., Lester, B., Du, N., Dai, A.M., Le, Q.V.: Finetuned language models are zero-shot learners. In: ICLR. OpenReview.net (2022)
2022
Cited alongside, same era.
2023
Cited alongside, same era.
Chalvatzaki, G., Younes, A., Nandha, D., Le, A.T., Ribeiro, L.F., Gurevych, I.: Learning to reason over scene graphs: a case study of finetuning gpt-2 into a robot language model for grounded task planning. Frontiers in Robotics and AI 10
2023
Cited alongside, same era.
Ray, P.P.: Chatgpt: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems (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: ICRA. pp. 11523–11530. IEEE (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Huang, C., Mees, O., Zeng, A., Burgard, W.: Visual language maps for robot navigation. In: ICRA. pp. 10608–10615. IEEE (2023)
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Later among the works it cites.
Wu, X., Duan, R., Ni, J.: Unveiling security, privacy, and ethical concerns of chatgpt. Journal of Information and Intelligence (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
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: ICLR. OpenReview.net (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Zhang, J., Liao, J., Hu, T., Zhou, T., Qian, H., Zhang, H., Li, H., Tang, L., Meng, Q., Song, W., Zhu, S.: Experience adapter: Adapting pre-trained language models for continual task planning. In: ICIRA (5). Lecture Notes in Computer Science, vol. 14271, pp. 389–400. Springer (2023)
2023
Later among the works it cites.