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Diligently gathered human demonstrations serve as the unsung heroes empowering the progression of robot learning.
Alvinn: An autonomous land vehicle in a neural network
D. A. Pomerleau · 1988
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Real-time collision avoidance in teleoperated whole-sensitive robot arm manipulators
V. J. Lumelsky and E. Cheung · 1993
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Beyond the web: Excavating the real world via mosaic
K. Goldberg, M. Mascha, S. Gentner, J. Tossman, N. Rothenberg, C. Sutter, and J. Wiegley · 1994
Earlier work this paper cites.
Bilateral teleoperation: An historical survey
P. F. Hokayem and M. W. Spong · 2006
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Robot programming by demonstration
A. Billard, S. Calinon, R. Dillmann, and S. Schaal · 2008
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An empirical evaluation of the system usability scale
A. Bangor, P. T. Kortum, and J. T. Miller · 2008
Earlier work this paper cites.
A survey of robot learning from demonstration
B. D. Argall, S. Chernova, M. Veloso, and B. Browning · 2009
Earlier work this paper cites.
Crowdsourcing for closed loop control
S. Osentoski, C. Crick, G. Jay, and O. C. Jenkins · 2010
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Strategies for human-in-the-loop robotic grasping
A. E. Leeper, K. Hsiao, M. Ciocarlie, L. Takayama, and D. Gossow · 2012
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Online customization of teleoperation interfaces
A. D. Dragan and S. S. Srinivasa · 2012
Earlier work this paper cites.
Novel interaction strategies for learning from teleoperation
B. Akgün, K. Subramanian, and A. L. Thomaz · 2012
Earlier work this paper cites.
A comparison of remote robot teleoperation interfaces for general object manipulation
D. Kent, C. Saldanha, and S. Chernova · 2017
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Comparing human-centric and robot-centric sampling for robot deep learning from demonstrations
M. Laskey, C. Chuck, J. Lee, J. Mahler, S. Krishnan, K. Jamieson, A. Dragan, and K. Goldberg · 2017
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Using dvrk teleoperation to facilitate deep learning of automation tasks for an industrial robot
J. Liang, J. Mahler, M. Laskey, P. Li, and K. Goldberg · 2017
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Baxter’s homunculus: Virtual reality spaces for teleoperation in manufacturing
J. I. Lipton, A. J. Fay, and D. Rus · 2017
Cited alongside, same era.
One-shot visual imitation learning via meta-learning
C. Finn, T. Yu, T. Zhang, P. Abbeel, and S. Levine · 2017
Cited alongside, same era.
Visual question answering: A survey of methods and datasets
Q. Wu, D. Teney, P. Wang, C. Shen, A. Dick, and A. van den Hengel · 2017
Cited alongside, same era.
Deep imitation learning for complex manipulation tasks from virtual reality teleoperation
T. Zhang, Z. McCarthy, O. Jow, D. Lee, X. Chen, K. Goldberg, and P. Abbeel · 2018
Cited alongside, same era.
Roboturk: A crowdsourcing platform for robotic skill learning through imitation
A. Mandlekar, Y. Zhu, A. Garg, J. Booher, M. Spero, A. Tung, J. Gao, J. Emmons, A. Gupta, E. Orbay, et al · 2018
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Hierarchical text-conditional image generation with clip latents
A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen · 2022
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A survey of embodied ai: From simulators to research tasks
J. Duan, S. Yu, H. L. Tan, H. Zhu, and C. Tan · 2022
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Perceiver-actor: A multi-task transformer for robotic manipulation
M. Shridhar, L. Manuelli, and D. Fox · 2022
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Do as i can, not as i say: Grounding language in robotic affordances
M. Ahn, A. Brohan, N. Brown, Y. Chebotar, O. Cortes, B. David, C. Finn, K. Gopalakrishnan, K. Hausman, A. Herzog, et al · 2022
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S. Dasari, F. Ebert, S. Tian, S. Nair, B. Bucher, K. Schmeckpeper, S. Singh, S. Levine, and C. Finn · 2019
Cited alongside, same era.
Comparing robot grasping teleoperation across desktop and virtual reality with ros reality
D. Whitney, E. Rosen, E. Phillips, G. Konidaris, and S. Tellex · 2019
Cited alongside, same era.
Grasping in the wild: Learning 6dof closed-loop grasping from low-cost demonstrations
S. Song, A. Zeng, J. Lee, and T. Funkhouser · 2020
Cited alongside, same era.
Rlbench: The robot learning benchmark & learning environment
S. James, Z. Ma, D. R. Arrojo, and A. J. Davison · 2020
Cited alongside, same era.
Visual imitation made easy, 2020
S. Young, D. Gandhi, S. Tulsiani, A. Gupta, P. Abbeel, and L. Pinto · 2020
Cited alongside, same era.
Learning transferable visual models from natural language supervision
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al · 2021
Cited alongside, same era.
Bridge data: Boosting generalization of robotic skills with cross-domain datasets
F. Ebert, Y. Yang, K. Schmeckpeper, B. Bucher, G. Georgakis, K. Daniilidis, C. Finn, and S. Levine · 2021
Cited alongside, same era.
Y. Jiang, A. Gupta, Z. Zhang, G. Wang, Y. Dou, Y. Chen, L. Fei-Fei, A. Anandkumar, Y. Zhu, and L. Fan · 2022
Later among the works it cites.
Rt-1: Robotics transformer for real-world control at scale
A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, J. Dabis, C. Finn, K. Gopalakrishnan, K. Hausman, A. Herzog, J. Hsu, et al · 2022
Later among the works it cites.
Perceiver-actor: A multi-task transformer for robotic manipulation
M. Shridhar, L. Manuelli, and D. Fox · 2022
Later among the works it cites.
Coarse-to-fine q-attention: Efficient learning for visual robotic manipulation via discretisation
S. James, K. Wada, T. Laidlow, and A. J. Davison · 2022
Later among the works it cites.
Towards an end-to-end framework for flow-guided video inpainting
Z. Li, C.-Z. Lu, J. Qin, C.-L. Guo, and M.-M. Cheng · 2022
Later among the works it cites.
Mimicplay: Long-horizon imitation learning by watching human play
C. Wang, L. Fan, J. Sun, R. Zhang, L. Fei-Fei, D. Xu, Y. Zhu, and A. Anandkumar · 2023
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Orbit: A unified simulation framework for interactive robot learning environments
M. Mittal, C. Yu, Q. Yu, J. Liu, N. Rudin, D. Hoeller, J. L. Yuan, R. Singh, Y. Guo, H. Mazhar, A. Mandlekar, B. Babich, G. State, M. Hutter, and A. Garg · 2023
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Imitating task and motion planning with visuomotor transformers, 2023
M. Dalal, A. Mandlekar, C. Garrett, A. Handa, R. Salakhutdinov, and D. Fox · 2023
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A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, et al · 2023
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