RobotGPT: Robot Manipulation Learning from ChatGPT

T. Kollar, S. Tellex, D. Roy, and N. Roy, “Toward understanding natural language directions,” in 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

2010

Earlier work this paper cites.

V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al

2015

Earlier work this paper cites.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition

2015

Earlier work this paper cites.

A. S. Lakshminarayanan, S. Ozair, and Y. Bengio, “Reinforcement learning with few expert demonstrations,” in NIPS workshop on deep learning for action and interaction

2016

Earlier work this paper cites.

T. Hester, M. Vecerik, O. Pietquin, M. Lanctot, T. Schaul, B. Piot, D. Horgan, J. Quan, A. Sendonaris, I. Osband, et al

2018

Earlier work this paper cites.

A. Zeng, S. Song, S. Welker, J. Lee, A. Rodriguez, and T. Funkhouser, “Learning synergies between pushing and grasping with self-supervised deep reinforcement learning,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

2018

Earlier work this paper cites.

S. Cabi, S. G. Colmenarejo, A. Novikov, K. Konyushkova, S. Reed, R. Jeong, K. Zolna, Y. Aytar, D. Budden, M. Vecerik, et al

Original

2019

Earlier work this paper cites.

S. Tellex, N. Gopalan, H. Kress-Gazit, and C. Matuszek, “Robots that use language,” Annual Review of Control, Robotics, and Autonomous Systems

2020

Earlier work this paper cites.

Y. Zhu, J. Wong, A. Mandlekar, R. Martín-Martín, A. Joshi, S. Nasiriany, and Y. Zhu, “robosuite: A modular simulation framework and benchmark for robot learning,” arXiv preprint arXiv:2009.12293

Original

2020

Earlier work this paper cites.

B. Delhaisse, L. Rozo, and D. G. Caldwell, “Pyrobolearn: A python framework for robot learning practitioners,” in Conference on Robot Learning

2020

Cited alongside, same era.

S. James, Z. Ma, D. R. Arrojo, and A. J. Davison, “Rlbench: The robot learning benchmark & learning environment,” IEEE Robotics and Automation Letters

2020

Cited alongside, same era.

D. Wang, C. Kohler, and R. Platt, “Policy learning in se (3) action spaces,” arXiv preprint arXiv:2010.02798

Original

2020

Cited alongside, same era.

O. Kroemer, S. Niekum, and G. Konidaris, “A review of robot learning for manipulation: Challenges, representations, and algorithms,” The Journal of Machine Learning Research

2021

Cited alongside, same era.

Y. Lee, E. S. Hu, and J. J. Lim, “Ikea furniture assembly environment for long-horizon complex manipulation tasks,” in 2021 ieee international conference on robotics and automation (icra)

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

Later among the works it cites.

D. Wang, R. Walters, X. Zhu, and R. Platt, “Equivariant q q learning in spatial action spaces,” in Conference on Robot Learning

2022

Later among the works it cites.

A. Zeng, S. Song, K.-T. Yu, E. Donlon, F. R. Hogan, M. Bauza, D. Ma, O. Taylor, M. Liu, E. Romo, et al

2022

Later among the works it cites.

S. Vemprala, R. Bonatti, A. Bucker, and A. Kapoor, “Chatgpt for robotics: Design principles and model abilities,” Microsoft Auton. Syst. Robot. Res

2023

Closest in time.

K. Lin, C. Agia, T. Migimatsu, M. Pavone, and J. Bohg, “Text2motion: From natural language instructions to feasible plans,” in ICRA2023 Workshop on Pretraining for Robotics (PT4R)