Fetching the paper…
Reading the bibliography…
We demonstrate the ability of large language models (LLMs) to perform iterative self-improvement of robot policies.
The convergence of the random search method in the extremal control of a many parameter system
LA Rastrigin · 1963
Earlier work this paper cites.
A simplex method for function minimization
John A Nelder and Roger Mead · 1965
Earlier work this paper cites.
Learning robot actions based on self-organising language memory
Stefan Wermter and Mark Elshaw · 2003
Earlier work this paper cites.
A holistic approach to compositional semantics: a connectionist model and robot experiments
Yuuya Sugita and Jun Tani · 2003
Earlier work this paper cites.
Policy gradient methods for robotics
Jan Peters and Stefan Schaal · 2006
Earlier work this paper cites.
A connectionist machine for genetic hillclimbing
David Ackley · 2012
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Gregory S. Corrado, and Jeffrey Dean · 2013
Earlier work this paper cites.
Learning to select and generalize striking movements in robot table tennis
Katharina Mülling, Jens Kober, Oliver Kroemer, and Jan Peters · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Language-conditioned imitation learning for robot manipulation tasks
Simon Stepputtis, Joseph Campbell, Mariano Phielipp, Stefan Lee, Chitta Baral, and Heni Ben Amor · 2020
Earlier work this paper cites.
Language Conditioned Imitation Learning Over Unstructured Data
Corey Lynch and Pierre Sermanet · 2021
Earlier work this paper cites.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou · 2022
Earlier work this paper cites.
Solving quantitative reasoning problems with language models
Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, et al · 2022
Cited alongside, same era.
Do as i can, not as i say: Grounding language in robotic affordances
Alex Irpan, Alexander Herzog, Alexander Toshkov Toshev, Andy Zeng, Anthony Brohan, Brian Andrew Ichter, Byron David, Carolina Parada, Chelsea Finn, Clayton Tan, Diego Reyes, Dmitry Kalashnikov, Eric Victor Jang, Fei Xia, Jarek Liam Rettinghouse, Jasmine Chiehju Hsu, Jornell Lacanlale Quiambao, Julian Ibarz, Kanishka Rao, Karol Hausman, Keerthana Gopalakrishnan, Kuang-Huei Lee, Kyle Alan Jeffrey, Linda Luu, Mengyuan Yan, Michael Soogil Ahn, Nicolas Sievers, Nikhil J Joshi, Noah Brown, Omar Eduardo Escareno Cortes, Peng Xu, Peter Pastor Sampedro, Pierre Sermanet, Rosario Jauregui Ruano, Ryan Christopher Julian, Sally Augusta Jesmonth, Sergey Levine, Steve Xu, Ted Xiao, Vincent Olivier Vanhoucke, Yao Lu, Yevgen Chebotar, and Yuheng Kuang · 2022
Cited alongside, same era.
Code as policies: Language model programs for embodied control
Jacky Liang, Wenlong Huang, Fei Xia, Peng Xu, Karol Hausman, Brian Ichter, Pete Florence, and Andy Zeng · 2022
Cited alongside, same era.
Repository-level prompt generation for large language models of code
Disha Shrivastava, Hugo Larochelle, and Daniel Tarlow · 2023
Large language models as optimizers
Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, and Xinyun Chen · 2023
Later among the works it cites.
Using large language models for hyperparameter optimization
Michael Zhang, Nishkrit Desai, Juhan Bae, Jonathan Lorraine, and Jimmy Ba · 2023
Later among the works it cites.
Robotic Table Tennis: A Case Study into a High Speed Learning System
David B D’Ambrosio, Navdeep Jaitly, Vikas Sindhwani, Ken Oslund, Peng Xu, Nevena Lazic, Anish Shankar, Tianli Ding, Jonathan Abelian, Erwin Coumans, Gus Kouretas, Thinh Nguyen, Justin Boyd, Atil Iscen, Reza Mahjourian, Vincent Vanhoucke, Alex Bewley, Yuheng Kuang, Michael Ahn, Deepali Jain, Satoshi Kataoka, Omar E Cortes, Pierre Sermanet, Corey Lynch, Pannag R Sanketi, Krzysztof Choromanski, Wenbo Gao, Juhana Kangaspunta, Krista Reymann, Grace Vesom, Sherry Q Moore, Avi Singh, Saminda W Abeyruwan, and Laura Graesser · 2023
Later among the works it cites.
i-sim2real: Reinforcement learning of robotic policies in tight human-robot interaction loops
Saminda Wishwajith Abeyruwan, Laura Graesser, David B D’Ambrosio, Avi Singh, Anish Shankar, Alex Bewley, Deepali Jain, Krzysztof Marcin Choromanski, and Pannag R Sanketi · 2023
Later among the works it cites.
Roco: Dialectic multi-robot collaboration with large language models
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Challenging big-bench tasks and whether chain-of-thought can solve them
Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V. Le, Ed H. Chi, Denny Zhou, and Jason Wei · 2023
Cited alongside, same era.
Large language models as general pattern machines
Suvir Mirchandani, Fei Xia, Pete Florence, brian ichter, Danny Driess, Montserrat Gonzalez Arenas, Kanishka Rao, Dorsa Sadigh, and Andy Zeng · 2023
Cited alongside, same era.
Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
Cited alongside, same era.
OpenAI · 2023
Cited alongside, same era.
Llm-planner: Few-shot grounded planning for embodied agents with large language models
Chan Hee Song, Jiaman Wu, Clayton Washington, Brian M Sadler, Wei-Lun Chao, and Yu Su · 2023
Cited alongside, same era.
Task and motion planning with large language models for object rearrangement
Yan Ding, Xiaohan Zhang, Chris Paxton, and Shiqi Zhang · 2023
Cited alongside, same era.
Mathematical discoveries from program search with large language models
Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Matej Balog, M. Kumar, Emilien Dupont, Francisco Ruiz, Jordan Ellenberg, Pengming Wang, Omar Fawzi, Pushmeet Kohli, and Alhussein Fawzi · 2023
Cited alongside, same era.
On the opportunities and risks of foundation models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, S. Buch, Dallas Card, Rodrigo Castellon, Niladri S. Chatterji, Annie S. Chen, Kathleen A. Creel, Jared Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren E. Gillespie, Karan Goel, Noah D. Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas F. Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, O. Khattab, Pang Wei Koh, Mark S. Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir P. Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Benjamin Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, J. F. Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, Aditi Raghunathan, Robert Reich, Hongyu Ren, Frieda Rong, Yusuf H. Roohani, Camilo Ruiz, Jack Ryan, Christopher R’e, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishna Parasuram Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei A. Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, and Percy Liang
Cited in the paper.
Zhao Mandi, Shreeya Jain, and Shuran Song · 2024
Later among the works it cites.
Learning to learn faster from human feedback with language model predictive control
Jacky Liang, Fei Xia, Wenhao Yu, Andy Zeng, Montse Gonzalez Arenas, Maria Attarian, Maria Bauzá, Matthew Bennice, Alex Bewley, Adil Dostmohamed, Chuyuan Fu, Nimrod Gileadi, Marissa Giustina, Keerthana Gopalakrishnan, Leonard Hasenclever, Jan Humplik, Jasmine Hsu, Nikhil J. Joshi, Ben Jyenis, Chase Kew, Sean Kirmani, Tsang-Wei Edward Lee, Kuang-Huei Lee, Assaf Hurwitz Michaely, Joss Moore, Kenneth Oslund, Dushyant Rao, Allen Ren, Baruch Tabanpour, Quan Ho Vuong, Ayzaan Wahid, Ted Xiao, Ying Xu, Vincent Zhuang, Peng Xu, Erik Frey, Ken Caluwaerts, Ting-Yu Zhang, Brian Ichter, Jonathan Tompson, Leila Takayama, Vincent Vanhoucke, Izhak Shafran, Maja Mataric, Dorsa Sadigh, Nicolas Manfred Otto Heess, Kanishka Rao, Nik Stewart, Jie Tan, and Carolina Parada · 2024
Later among the works it cites.
Eureka: Human-level reward design via coding large language models
Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan, and Anima Anandkumar · 2024
Later among the works it cites.
In-context imitation learning via next-token prediction
Letian Fu, Huang Huang, Gaurav Datta, Lawrence Yunliang Chen, William Chung-Ho Panitch, Fangchen Liu, Hui Li, and Ken Goldberg · 2024
Later among the works it cites.
Keypoint Action Tokens Enable In-Context Imitation Learning in Robotics
Norman Di Palo and Edward Johns · 2024
Later among the works it cites.
Learning to learn faster from human feedback with language model predictive control, 2024
Jacky Liang, Fei Xia, Wenhao Yu, Andy Zeng, Montserrat Gonzalez Arenas, Maria Attarian, Maria Bauza, Matthew Bennice, Alex Bewley, Adil Dostmohamed, Chuyuan Kelly Fu, Nimrod Gileadi, Marissa Giustina, Keerthana Gopalakrishnan, Leonard Hasenclever, Jan Humplik, Jasmine Hsu, Nikhil Joshi, Ben Jyenis, Chase Kew, Sean Kirmani, Tsang-Wei Edward Lee, Kuang-Huei Lee, Assaf Hurwitz Michaely, Joss Moore, Ken Oslund, Dushyant Rao, Allen Ren, Baruch Tabanpour, Quan Vuong, Ayzaan Wahid, Ted Xiao, Ying Xu, Vincent Zhuang, Peng Xu, Erik Frey, Ken Caluwaerts, Tingnan Zhang, Brian Ichter, Jonathan Tompson, Leila Takayama, Vincent Vanhoucke, Izhak Shafran, Maja Mataric, Dorsa Sadigh, Nicolas Heess, Kanishka Rao, Nik Stewart, Jie Tan, and Carolina Parada · 2024
Later among the works it cites.
Achieving human level competitive robot table tennis
David B. D’Ambrosio, Saminda Abeyruwan, Laura Graesser, Atil Iscen, Heni Ben Amor, Alex Bewley, Barney J. Reed, Krista Reymann, Leila Takayama, Yuval Tassa, Krzysztof Choromanski, Erwin Coumans, Deepali Jain, Navdeep Jaitly, Natasha Jaques, Satoshi Kataoka, Yuheng Kuang, Nevena Lazic, Reza Mahjourian, Sherry Moore, Kenneth Oslund, Anish Shankar, Vikas Sindhwani, Vincent Vanhoucke, Grace Vesom, Peng Xu, and Pannag R. Sanketi · 2024
Later among the works it cites.