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Enabling robots to learn novel visuomotor skills in a data-efficient manner remains an unsolved problem with myriad challenges.
Interactive imitation learning in robotics: A survey
Carlos Celemin, Rodrigo Pérez-Dattari, Eugenio Chisari, Giovanni Franzese, Leandro de Souza Rosa, Ravi Prakash, Zlatan Ajanović, Marta Ferraz, Abhinav Valada, and Jens Kober · 1935
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Alvinn: An autonomous land vehicle in a neural network
Dean Pomerleau · 1989
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Learning movement primitives
Stefan Schaal, Jan Peters, Jun Nakanishi, and Auke Ijspeert · 2005
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A survey of robot learning from demonstration
Brenna D. Argall, Sonia Chernova, Manuela Veloso, and Brett Browning · 2008
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey J. Gordon, and J. Andrew Bagnell · 2011
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One-shot imitation learning
Yan Duan, Marcin Andrychowicz, Bradly Stadie, OpenAI Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, and Wojciech Zaremba · 2017
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One-shot visual imitation learning via meta-learning
Chelsea Finn, Tianhe Yu, T. Zhang, P. Abbeel, and Sergey Levine · 2017
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Imitation learning: A survey of learning methods
Ahmed Hussein, Mohamed Medhat Gaber, Eyad Elyan, and Chrisina Jayne · 2017
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Dart: Noise injection for robust imitation learning
Michael Laskey, Jonathan Lee, Roy Fox, Anca D. Dragan, and Ken Goldberg · 2017
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Visual foresight: Model-based deep reinforcement learning for vision-based robotic control
Frederik Ebert, Chelsea Finn, Sudeep Dasari, Annie Xie, Alex Lee, and Sergey Levine · 2018
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Task-embedded control networks for few-shot imitation learning
Stephen James, Michael Bloesch, and Andrew J Davison · 2018
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Hg-dagger: Interactive imitation learning with human experts
Michael Kelly, Chelsea Sidrane, K. Driggs-Campbell, and Mykel J. Kochenderfer · 2018
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One-shot hierarchical imitation learning of compound visuomotor tasks
Tianhe Yu, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2018
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Goal-conditioned imitation learning
Yiming Ding, Carlos Florensa, Pieter Abbeel, and Mariano Phielipp · 2019
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Learning latent plans from play
Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, and Pierre Sermanet · 2019
Cited alongside, same era.
Watch, try, learn: Meta-learning from demonstrations and reward
Allan Zhou, Eric Jang, Daniel Kappler, Alex Herzog, Mohi Khansari, Paul Wohlhart, Yunfei Bai, Mrinal Kalakrishnan, Sergey Levine, and Chelsea Finn · 2019
Cited alongside, same era.
Human-in-the-loop imitation learning using remote teleoperation
Ajay Mandlekar, Danfei Xu, Roberto Mart’in-Mart’in, Yuke Zhu, Li Fei-Fei, and Silvio Savarese · 2020
Cited alongside, same era.
Learning from interventions: Human-robot interaction as both explicit and implicit feedback
Jonathan Spencer, Sanjiban Choudhury, Matt Barnes, Matthew Schmittle, Mung Chiang, Peter Ramadge, and Siddhartha Srinivasa · 2020
Cited alongside, same era.
Transporter networks: Rearranging the visual world for robotic manipulation
Andy Zeng, Pete Florence, Jonathan Tompson, Stefan Welker, Jonathan Chien, Maria Attarian, Travis Armstrong, Ivan Krasin, Dan Duong, Vikas Sindhwani, and Johnny Lee · 2020
Rt-1: Robotics transformer for real-world control at scale
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alexander Herzog, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Tomas Jackson, Sally Jesmonth, Nikhil J. Joshi, Ryan C. Julian, Dmitry Kalashnikov, Yuheng Kuang, Isabel Leal, Kuang-Huei Lee, Sergey Levine, Yao Lu, Utsav Malla, Deeksha Manjunath, Igor Mordatch, Ofir Nachum, Carolina Parada, Jodilyn Peralta, Emily Perez, Karl Pertsch, Jornell Quiambao, Kanishka Rao, Michael S. Ryoo, Grecia Salazar, Pannag R. Sanketi, Kevin Sayed, Jaspiar Singh, Sumedh Anand Sontakke, Austin Stone, Clayton Tan, Huong Tran, Vincent Vanhoucke, Steve Vega, Quan Ho Vuong, F. Xia, Ted Xiao, Peng Xu, Sichun Xu, Tianhe Yu, and Brianna Zitkovich · 2022
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Contrastive learning as goal-conditioned reinforcement learning
Benjamin Eysenbach, Tianjun Zhang, Ruslan Salakhutdinov, and Sergey Levine · 2022
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Eliciting compatible demonstrations for multi-human imitation learning
Kanishk Gandhi, Siddharth Karamcheti, Madeline Liao, and Dorsa Sadigh · 2022
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Cited alongside, same era.
robosuite: A modular simulation framework and benchmark for robot learning
Yuke Zhu, Josiah Wong, Ajay Mandlekar, Roberto Martín-Martín, Abhishek Joshi, Soroush Nasiriany, and Yifeng Zhu · 2020
Cited alongside, same era.
Bridge data: Boosting generalization of robotic skills with cross-domain datasets
Frederik Ebert, Yanlai Yang, Karl Schmeckpeper, Bernadette Bucher, Georgios Georgakis, Kostas Daniilidis, Chelsea Finn, and Sergey Levine · 2021
Cited alongside, same era.
Coarse-to-fine imitation learning: Robot manipulation from a single demonstration
Edward Johns · 2021
Cited alongside, same era.
Towards more generalizable one-shot visual imitation learning
Zhao Mandi, Fangchen Liu, Kimin Lee, and P. Abbeel · 2021
Cited alongside, same era.
What matters in learning from offline human demonstrations for robot manipulation
Ajay Mandlekar, Danfei Xu, Josiah Wong, Soroush Nasiriany, Chen Wang, Rohun Kulkarni, Li Fei-Fei, Silvio Savarese, Yuke Zhu, and Roberto Martín-Martín · 2021
Cited alongside, same era.
Cliport: What and where pathways for robotic manipulation
Mohit Shridhar, Lucas Manuelli, and Dieter Fox · 2021
Cited alongside, same era.
Parrot: Data-driven behavioral priors for reinforcement learning
Avi Singh, Huihan Liu, Gaoyue Zhou, Albert Yu, Nicholas Rhinehart, and Sergey Levine · 2021
Cited alongside, same era.
Anirudh Goyal, Abram L. Friesen, Andrea Banino, Théophane Weber, Nan Rosemary Ke, Adrià Puigdomènech Badia, Arthur Guez, Mehdi Mirza, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy P. Lillicrap, Nicolas Manfred Otto Heess, and Charles Blundell · 2022
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Coarse-to-fine q-attention with tree expansion
Stephen James and P. Abbeel · 2022
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Q-attention: Enabling efficient learning for vision-based robotic manipulation
Stephen James and Andrew J. Davison · 2022
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Pre-training for robots: Offline rl enables learning new tasks from a handful of trials
Aviral Kumar, Anika Singh, Frederik Ebert, Yanlai Yang, Chelsea Finn, and Sergey Levine · 2022
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Vip: Towards universal visual reward and representation via value-implicit pre-training
Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman, Osbert Bastani, Vikash Kumar, and Amy Zhang · 2022
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R3m: A universal visual representation for robot manipulation
Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, and Abhi Gupta · 2022
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Learning and retrieval from prior data for skill-based imitation learning
Soroush Nasiriany, Tian Gao, Ajay Mandlekar, and Yuke Zhu · 2022
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The unsurprising effectiveness of pre-trained vision models for control
Simone Parisi, Aravind Rajeswaran, Senthil Purushwalkam, and Abhinav Kumar Gupta · 2022
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Reil: A framework for reinforced intervention-based imitation learning, 2022
Rom Parnichkun, Matthew N. Dailey, and Atsushi Yamashita · 2022
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Real-world robot learning with masked visual pre-training
Ilija Radosavovic, Tete Xiao, Stephen James, Pieter Abbeel, Jitendra Malik, and Trevor Darrell · 2022
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Reciprocal MIND MELD: Improving learning from demonstration via personalized, reciprocal teaching
Mariah L Schrum, Erin Hedlund-Botti, and Matthew Gombolay · 2022
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Masked visual pre-training for motor control
Tete Xiao, Ilija Radosavovic, Trevor Darrell, and Jitendra Malik · 2022
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