Visual Backtracking Teleoperation: A Data Collection Protocol for Offline Image-Based Reinforcement Learning

D. A. Pomerleau, “Alvinn: An autonomous land vehicle in a neural network,” in NIPS

1988

Earlier work this paper cites.

S. Schaal, “Learning from demonstration,” Advances in neural information processing systems

1996

Earlier work this paper cites.

B. D. Argall, S. Chernova, M. Veloso, and B. Browning, “A survey of robot learning from demonstration,” Robotics and autonomous systems

2009

Earlier work this paper cites.

S. Ross, G. Gordon, and D. Bagnell, “A reduction of imitation learning and structured prediction to no-regret online learning,” in Proceedings of the fourteenth international conference on artificial intelligence and statistics

2011

Earlier work this paper cites.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980

Original

2014

Earlier work this paper cites.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition

2016

Earlier work this paper cites.

J. Bradbury, R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, G. Necula, A. Paszke, J. VanderPlas, S. Wanderman-Milne, and Q. Zhang, “JAX: composable transformations of Python+NumPy programs,” 2018

2018

Earlier work this paper cites.

S. Fujimoto, D. Meger, and D. Precup, “Off-policy deep reinforcement learning without exploration,” in International conference on machine learning

2019

Earlier work this paper cites.

M. Kelly, C. Sidrane, K. Driggs-Campbell, and M. J. Kochenderfer, “Hg-dagger: Interactive imitation learning with human experts,” in 2019 International Conference on Robotics and Automation (ICRA)

2019

Cited alongside, same era.

J. Chen and N. Jiang, “Information-theoretic considerations in batch reinforcement learning,” in International Conference on Machine Learning

2019

Cited alongside, same era.

S. Levine, A. Kumar, G. Tucker, and J. Fu, “Offline reinforcement learning: Tutorial, review, and perspectives on open problems,” arXiv preprint arXiv:2005.01643

Original

2020

Cited alongside, same era.

A. Nair, A. Gupta, M. Dalal, and S. Levine, “Awac: Accelerating online reinforcement learning with offline datasets,” arXiv preprint arXiv:2006.09359

Original

2020

Cited alongside, same era.

A. Mandlekar, D. Xu, R. Mart’in-Mart’in, Y. Zhu, L. Fei-Fei, and S. Savarese, “Human-in-the-loop imitation learning using remote teleoperation,” ArXiv

A. Kumar, J. Hong, A. Singh, and S. Levine, “Should i run offline reinforcement learning or behavioral cloning?,” in Deep RL Workshop NeurIPS 2021

2021

Later among the works it cites.

D. Kalashnikov, J. Varley, Y. Chebotar, B. Swanson, R. Jonschkowski, C. Finn, S. Levine, and K. Hausman, “Mt-opt: Continuous multi-task robotic reinforcement learning at scale,” arXiv preprint arXiv:2104.08212

Original

2021

Later among the works it cites.

R. Hoque, A. Balakrishna, C. Putterman, M. Luo, D. S. Brown, D. Seita, B. Thananjeyan, E. Novoseller, and K. Goldberg, “Lazydagger: Reducing context switching in interactive imitation learning,” in 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)

2021

Later among the works it cites.

M. Ahn, A. Brohan, N. Brown, Y. Chebotar, O. Cortes, B. David, C. Finn, K. Gopalakrishnan, K. Hausman, A. Herzog, et al

Original

2022

Closest in time.