Interactively Robot Action Planning with Uncertainty Analysis and Active Questioning by Large Language Model

K. Choromanski, V. Likhosherstov, D. Dohan, X. Song, A. Gane, T. Sarlos, P. Hawkins, J. Davis, D. Belanger, L. Colwell et al. , “Masked language modeling for proteins via linearly scalable long-context transformers,” arXiv preprint arXiv:2006.03555 , 2020

Original

2020

Earlier work this paper cites.

A. Katharopoulos, A. Vyas, N. Pappas, and F. Fleuret, “Transformers are rnns: Fast autoregressive transformers with linear attention,” in in Proceedings of the International Conference on Machine Learning , 2020, pp. 5156–5165

2020

Earlier work this paper cites.

C. Dong, L. Yu, M. Takizawa, S. Kudoh, and T. Suehiro, “Food peeling method for dual-arm cooking robot,” in 2021 IEEE/SICE International Symposium on System Integration (SII) , 2021, pp. 801–806

2021

Earlier work this paper cites.

M. Toyoda, K. Suzuki, H. Mori, Y. Hayashi, and T. Ogata, “Embodying pre-trained word embeddings through robot actions,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 4225–4232, 2021

2021

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 arXiv preprint arXiv:2209.07753 , 2022

Original

2022

Earlier work this paper cites.

A. Brohan, Y. Chebotar, C. Finn, K. Hausman, A. Herzog, D. Ho, J. Ibarz, A. Irpan, E. Jang, R. Julian et al. , “Do as i can, not as i say: Grounding language in robotic affordances,” in in Proceedings of the Conference on Robot Learning , 2022

2022

Earlier work this paper cites.

A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, J. Dabis, C. Finn, K. Gopalakrishnan, K. Hausman, A. Herzog, J. Hsu, J. Ibarz, B. Ichter, A. Irpan, T. Jackson, S. Jesmonth, N. Joshi, R. Julian, D. Kalashnikov, Y. Kuang, I. Leal, K.-H. Lee, S. Levine, Y. Lu, U. Malla, D. Manjunath, I. Mordatch, O. Nachum, C. Parada, J. Peralta, E. Perez, K. Pertsch, J. Quiambao, K. Rao, M. Ryoo, G. Salazar, P. Sanketi, K. Sayed, J. Singh, S. Sontakke, A. Stone, C. Tan, H. Tran, V. Vanhoucke, S. Vega, Q. Vuong, F. Xia, T. Xiao, P. Xu, S. Xu, T. Yu, and B. Zitkovich, “Rt-1: Robotics transformer for real-world control at scale,” in arXiv preprint arXiv:2212.06817 , 2022

Original

2022

Earlier work this paper cites.

T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa, “Large language models are zero-shot reasoners,” Advances in neural information processing systems , vol. 35, pp. 22 199–22 213, 2022

2022

Cited alongside, same era.

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 , 2022, pp. 9118–9147

2022

Cited alongside, same era.

J. Liu, Y. Chen, Z. Dong, S. Wang, S. Calinon, M. Li, and F. Chen, “Robot cooking with stir-fry: Bimanual non-prehensile manipulation of semi-fluid objects,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 5159–5166, 2022

2022

Cited alongside, same era.

M. Toyoda, K. Suzuki, Y. Hayashi, and T. Ogata, “Learning bidirectional translation between descriptions and actions with small paired data,” IEEE Robotics and Automation Letters , vol. 7, no. 4, pp. 10 930–10 937, 2022

2022

Cited alongside, same era.

K. Suzuki, H. Ito, T. Yamada, K. Kase, and T. Ogata, “Deep predictive learning : Motion learning concept inspired by cognitive robotics,” arXiv preprint arXiv:2306.14714 , 2023

Original

2023

Closest in time.

OpenAI, “Chatgpt,” Accessed:2023-06-07, https://openai.com/blog/chatgpt/

2023

Closest in time.

K. Kawaharazuka, Y. Obinata, N. Kanazawa, K. Okada, and M. Inaba, “Vqa-based robotic state recognition optimized with genetic algorithm,” in in Proceedings of the IEEE International Conference on Robotics and Automation , 2023

2023

Closest in time.

Y. J. Ma, W. Liang, V. Som, V. Kumar, A. Zhang, O. Bastani, and D. Jayaraman, “Liv: Language-image representations and rewards for robotic control,” 2023

2023

Closest in time.

S. Vemprala, R. Bonatti, A. Bucker, and A. Kapoor, “Chatgpt for robotics: Design principles and model abilities,” 2023