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Imitation learning is a powerful machine learning algorithm for a robot to acquire manipulation skills.
Locally weighted regression: an approach to regression analysis by local fitting
William S Cleveland and Susan J Devlin · 1988
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Alvinn: An autonomous land vehicle in a neural network
Dean A Pomerleau · 1988
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Distance minimization for reward learning from scored trajectories
Benjamin Burchfiel, Carlo Tomasi, and Ronald Parr · 2016
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Deep spatial autoencoders for visuomotor learning
Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, and Pieter Abbeel · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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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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Semi-parametric topological memory for navigation
Nikolay Savinov, Alexey Dosovitskiy, and Vladlen Koltun · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations
Daniel Brown, Wonjoon Goo, Prabhat Nagarajan, and Scott Niekum · 2019
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Robust learning from demonstrations with mixed qualities using leveraged gaussian processes
Sungjoon Choi, Kyungjae Lee, and Songhwai Oh · 2019
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Search on the replay buffer: Bridging planning and reinforcement learning
Ben Eysenbach, Russ R Salakhutdinov, and Sergey Levine · 2019
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Stabilizing off-policy q-learning via bootstrapping error reduction
Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, and Sergey Levine · 2019
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Advantage-weighted regression: Simple and scalable off-policy reinforcement learning
Xue Bin Peng, Aviral Kumar, Grace Zhang, and Sergey Levine · 2019
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Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 2019
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Sparse graphical memory for robust planning
Scott Emmons, Ajay Jain, Misha Laskin, Thanard Kurutach, Pieter Abbeel, and Deepak Pathak · 2020
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Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
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Awac: Accelerating online reinforcement learning with offline datasets
Ashvin Nair, Abhishek Gupta, Murtaza Dalal, and Sergey Levine · 2020
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Discriminator-weighted offline imitation learning from suboptimal demonstrations
Haoran Xu, Xianyuan Zhan, Honglei Yin, and Huiling Qin · 2022
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Value memory graph: A graph-structured world model for offline reinforcement learning
Deyao Zhu, Li Erran Li, and Mohamed Elhoseiny · 2022
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Rt-2: Vision-language-action models transfer web knowledge to robotic control
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, et al · 2023
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Diffusion policy: Visuomotor policy learning via action diffusion
Cheng Chi, Siyuan Feng, Yilun Du, Zhenjia Xu, Eric Cousineau, Benjamin Burchfiel, and Shuran Song · 2023
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Behavior retrieval: Few-shot imitation learning by querying unlabeled datasets
Maximilian Du, Suraj Nair, Dorsa Sadigh, and Chelsea Finn · 2023
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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A minimalist approach to offline reinforcement learning
Scott Fujimoto and Shixiang Shane Gu · 2021
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Offline reinforcement learning as one big sequence modeling problem
Michael Janner, Qiyang Li, and Sergey Levine · 2021
Cited alongside, same era.
Offline reinforcement learning with implicit q-learning
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 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
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The surprising effectiveness of representation learning for visual imitation
Jyothish Pari, Nur Muhammad Shafiullah, Sridhar Pandian Arunachalam, and Lerrel Pinto · 2021
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Imitation learning by estimating expertise of demonstrators
Mark Beliaev, Andy Shih, Stefano Ermon, Dorsa Sadigh, and Ramtin Pedarsani · 2022
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Few-shot preference learning for human-in-the-loop rl
Donald Joseph Hejna III and Dorsa Sadigh · 2023
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Furniturebench: Reproducible real-world benchmark for long-horizon complex manipulation
Minho Heo, Youngwoon Lee, Doohyun Lee, and Joseph J Lim · 2023
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Learning to discern: Imitating heterogeneous human demonstrations with preference and representation learning
Sachit Kuhar, Shuo Cheng, Shivang Chopra, Matthew Bronars, and Danfei Xu · 2023
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Perceiver-actor: A multi-task transformer for robotic manipulation
Mohit Shridhar, Lucas Manuelli, and Dieter Fox · 2023
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Gello: A general, low-cost, and intuitive teleoperation framework for robot manipulators
Philipp Wu, Yide Shentu, Zhongke Yi, Xingyu Lin, and Pieter Abbeel · 2023
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Discriminator-guided model-based offline imitation learning
Wenjia Zhang, Haoran Xu, Haoyi Niu, Peng Cheng, Ming Li, Heming Zhang, Guyue Zhou, and Xianyuan Zhan · 2023
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Learning fine-grained bimanual manipulation with low-cost hardware
Tony Z Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn · 2023
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On the effectiveness of retrieval, alignment, and replay in manipulation
Norman Di Palo and Edward Johns · 2024
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Where are we in the search for an artificial visual cortex for embodied intelligence?
Arjun Majumdar, Karmesh Yadav, Sergio Arnaud, Jason Ma, Claire Chen, Sneha Silwal, Aryan Jain, Vincent-Pierre Berges, Tingfan Wu, Jay Vakil, et al · 2024
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