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Recent advancements in large-scale multi-task robot learning offer the potential for deploying robot fleets in household and industrial settings, enabling them to perform diverse tasks across various environments.
Neural networks for prediction of robot failures
A. Diryag, M. Mitić, and Z. Miljković · 2014
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U-net: Convolutional networks for biomedical image segmentation, 2015
O. Ronneberger, P. Fischer, and T. Brox · 2015
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Learning structured output representation using deep conditional generative models
K. Sohn, H. Lee, and X. Yan · 2015
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Safe visual navigation via deep learning and novelty detection
C. Richter and N. Roy · 2017
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QT-Opt: Scalable deep reinforcement learning for vision-based robotic manipulation
D. Kalashnikov, A. Irpan, P. Pastor, J. Ibarz, A. Herzog, E. Jang, D. Quillen, E. Holly, M. Kalakrishnan, V. Vanhoucke, and S. Levine · 2018
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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, S. Savarese, and L. Fei-Fei · 2018
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Hg-dagger: Interactive imitation learning with human experts
M. Kelly, C. Sidrane, K. Driggs-Campbell, and M. J. Kochenderfer · 2019
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Fast run-time monitoring, replanning, and recovery for safe autonomous system operations
E. Yel and N. Bezzo · 2019
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EnsembleDagger: A bayesian approach to safe imitation learning
K. Menda, K. Driggs-Campbell, and M. J. Kochenderfer · 2019
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Human-in-the-loop imitation learning using remote teleoperation
A. Mandlekar, D. Xu, R. Martín-Martín, Y. Zhu, L. Fei-Fei, and S. Savarese · 2020
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Learning from interventions: Human-robot interaction as both explicit and implicit feedback
J. Spencer, S. Choudhury, M. Barnes, M. Schmittle, M. Chiang, P. Ramadge, and S. Srinivasa · 2020
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Denoising diffusion probabilistic models, 2020
J. Ho, A. Jain, and P. Abbeel · 2020
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Scaled autonomy: Enabling human operators to control robot fleets, 2020
G. Swamy, S. Reddy, S. Levine, and A. D. Dragan · 2020
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Correct me if i am wrong: Interactive learning for robotic manipulation
E. Chisari, T. Welschehold, J. Boedecker, W. Burgard, and A. Valada · 2021
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Lazydagger: Reducing context switching in interactive imitation learning
R. Hoque, A. Balakrishna, C. Putterman, M. Luo, D. S. Brown, D. Seita, B. Thananjeyan, E. Novoseller, and K. Goldberg · 2021
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M. Salehi, H. Mirzaei, D. Hendrycks, Y. Li, M. H. Rohban, and M. Sabokrou · 2021
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What matters in learning from offline human demonstrations for robot manipulation, 2021
A. Mandlekar, D. Xu, J. Wong, S. Nasiriany, C. Wang, R. Kulkarni, L. Fei-Fei, S. Savarese, Y. Zhu, and R. Martín-Martín · 2021
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Bridge data: Boosting generalization of robotic skills with cross-domain datasets, 2021
F. Ebert, Y. Yang, K. Schmeckpeper, B. Bucher, G. Georgakis, K. Daniilidis, C. Finn, and S. Levine · 2021
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Rt-1: Robotics transformer for real-world control at scale, 2022
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. J. 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 · 2022
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A generalist agent, 2022
S. Reed, K. Zolna, E. Parisotto, S. G. Colmenarejo, A. Novikov, G. Barth-Maron, M. Gimenez, Y. Sulsky, J. Kay, J. T. Springenberg, T. Eccles, J. Bruce, A. Razavi, A. Edwards, N. Heess, Y. Chen, R. Hadsell, O. Vinyals, M. Bordbar, and N. de Freitas · 2022
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Fleet-dagger: Interactive robot fleet learning with scalable human supervision, 2022
R. Hoque, L. Y. Chen, S. Sharma, K. Dharmarajan, B. Thananjeyan, P. Abbeel, and K. Goldberg · 2022
Cited alongside, same era.
Efficient learning of safe driving policy via human-ai copilot optimization, 2022
Q. Li, Z. Peng, and B. Zhou · 2022
Cited alongside, same era.
Error-aware imitation learning from teleoperation data for mobile manipulation
J. Wong, A. Tung, A. Kurenkov, A. Mandlekar, L. Fei-Fei, S. Savarese, and R. Martín-Martín · 2022
Cited alongside, same era.
Pato: Policy assisted teleoperation for scalable robot data collection, 2022
S. Dass, K. Pertsch, H. Zhang, Y. Lee, J. J. Lim, and S. Nikolaidis · 2022
Cited alongside, same era.
Bc-z: Zero-shot task generalization with robotic imitation learning, 2022
E. Jang, A. Irpan, M. Khansari, D. Kappler, F. Ebert, C. Lynch, S. Levine, and C. Finn · 2022
Cited alongside, same era.
Robots that ask for help: Uncertainty alignment for large language model planners, 2023
A. Z. Ren, A. Dixit, A. Bodrova, S. Singh, S. Tu, N. Brown, P. Xu, L. Takayama, F. Xia, J. Varley, Z. Xu, D. Sadigh, A. Zeng, and A. Majumdar · 2023
Later among the works it cites.
Reflect: Summarizing robot experiences for failure explanation and correction, 2023
Z. Liu, A. Bahety, and S. Song · 2023
Later among the works it cites.
Daydreamer: World models for physical robot learning
P. Wu, A. Escontrela, D. Hafner, P. Abbeel, and K. Goldberg · 2023
Later among the works it cites.
Attention is all you need, 2023
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin · 2023
Later among the works it cites.
Robot fleet learning via policy merging, 2024
L. Wang, K. Zhang, A. Zhou, M. Simchowitz, and R. Tedrake · 2024
Closest in time.
Openbot-fleet: A system for collective learning with real robots, 2024
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A system-level view on out-of-distribution data in robotics, 2022
R. Sinha, A. Sharma, S. Banerjee, T. Lew, R. Luo, S. M. Richards, Y. Sun, E. Schmerling, and M. Pavone · 2022
Cited alongside, same era.
When to ask for help: Proactive interventions in autonomous reinforcement learning, 2022
A. Xie, F. Tajwar, A. Sharma, and C. Finn · 2022
Cited alongside, same era.
Model-based imitation learning for urban driving, 2022
A. Hu, G. Corrado, N. Griffiths, Z. Murez, C. Gurau, H. Yeo, A. Kendall, R. Cipolla, and J. Shotton · 2022
Cited alongside, same era.
Vint: A foundation model for visual navigation, 2023
D. Shah, A. Sridhar, N. Dashora, K. Stachowicz, K. Black, N. Hirose, and S. Levine · 2023
Cited alongside, same era.
Fleet-dagger: Interactive robot fleet learning with scalable human supervision
R. Hoque, L. Y. Chen, S. Sharma, K. Dharmarajan, B. Thananjeyan, P. Abbeel, and K. Goldberg · 2023
Cited alongside, same era.
Robot learning on the job: Human-in-the-loop autonomy and learning during deployment
H. Liu, S. Nasiriany, L. Zhang, Z. Bao, and Y. Zhu · 2023
Cited alongside, same era.
Gaia-1: A generative world model for autonomous driving, 2023
A. Hu, L. Russell, H. Yeo, Z. Murez, G. Fedoseev, A. Kendall, J. Shotton, and G. Corrado · 2023
Cited alongside, same era.
M. Müller, S. Brahmbhatt, A. Deka, Q. Leboutet, D. Hafner, and V. Koltun · 2024
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Model-based runtime monitoring with interactive imitation learning
H. Liu, S. Dass, R. Martín-Martín, and Y. Zhu · 2024
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Video generation models as world simulators
T. Brooks, B. Peebles, C. Holmes, W. DePue, Y. Guo, L. Jing, D. Schnurr, J. Taylor, T. Luhman, E. Luhman, C. Ng, R. Wang, and A. Ramesh · 2024
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Genie: Generative interactive environments, 2024
J. Bruce, M. Dennis, A. Edwards, J. Parker-Holder, Y. Shi, E. Hughes, M. Lai, A. Mavalankar, R. Steigerwald, C. Apps, Y. Aytar, S. Bechtle, F. Behbahani, S. Chan, N. Heess, L. Gonzalez, S. Osindero, S. Ozair, S. Reed, J. Zhang, K. Zolna, J. Clune, N. de Freitas, S. Singh, and T. Rocktäschel · 2024
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Pandora: Towards general world model with natural language actions and video states
J. Xiang, G. Liu, Y. Gu, Q. Gao, Y. Ning, Y. Zha, Z. Feng, T. Tao, S. Hao, Y. Shi, Z. Liu, E. P. Xing, and Z. Hu · 2024
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Octo: An open-source generalist robot policy, 2024
O. M. Team, D. Ghosh, H. Walke, K. Pertsch, K. Black, O. Mees, S. Dasari, J. Hejna, T. Kreiman, C. Xu, J. Luo, Y. L. Tan, L. Y. Chen, P. Sanketi, Q. Vuong, T. Xiao, D. Sadigh, C. Finn, and S. Levine · 2024
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Diffusion policy: Visuomotor policy learning via action diffusion, 2024
C. Chi, Z. Xu, S. Feng, E. Cousineau, Y. Du, B. Burchfiel, R. Tedrake, and S. Song · 2024
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Open X-Embodiment: Robotic learning datasets and RT-X models
Open X-Embodiment Collaboration et al · 2024
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Droid: A large-scale in-the-wild robot manipulation dataset, 2024
A. Khazatsky, K. Pertsch, S. Nair, A. Balakrishna, S. Dasari, S. Karamcheti, S. Nasiriany, M. K. Srirama, L. Y. Chen, et al · 2024
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Robocasa: Large-scale simulation of everyday tasks for generalist robots
S. Nasiriany, A. Maddukuri, L. Zhang, A. Parikh, A. Lo, A. Joshi, A. Mandlekar, and Y. Zhu · 2024
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Aloha 2: An enhanced low-cost hardware for bimanual teleoperation, 2024
A. . Team, J. Aldaco, T. Armstrong, R. Baruch, J. Bingham, S. Chan, K. Draper, D. Dwibedi, C. Finn, P. Florence, S. Goodrich, W. Gramlich, T. Hage, A. Herzog, J. Hoech, T. Nguyen, I. Storz, B. Tabanpour, L. Takayama, J. Tompson, A. Wahid, T. Wahrburg, S. Xu, S. Yaroshenko, K. Zakka, and T. Z. Zhao · 2024
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Autort: Embodied foundation models for large scale orchestration of robotic agents, 2024
M. Ahn, D. Dwibedi, C. Finn, M. G. Arenas, K. Gopalakrishnan, K. Hausman, B. Ichter, A. Irpan, N. Joshi, R. Julian, S. Kirmani, I. Leal, E. Lee, S. Levine, Y. Lu, I. Leal, S. Maddineni, K. Rao, D. Sadigh, P. Sanketi, P. Sermanet, Q. Vuong, S. Welker, F. Xia, T. Xiao, P. Xu, S. Xu, and Z. Xu · 2024
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Not all errors are made equal: A regret metric for detecting system-level trajectory prediction failures
K. Nakamura, R. Tian, and A. Bajcsy · 2024
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”task success” is not enough: Investigating the use of video-language models as behavior critics for catching undesirable agent behaviors, 2024
L. Guan, Y. Zhou, D. Liu, Y. Zha, H. B. Amor, and S. Kambhampati · 2024
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