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Imitation learning has proven to be a powerful tool for training complex visuomotor policies.
Dream to control: Learning behaviors by latent imagination
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Inverse-dynamics model eye movement control by purkinje cells in the cerebellum
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Teaching and learning of deburring robots using neural networks
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Robot programming by human demonstration: Adaptation and inconsistency in constrained motion
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Building elementary robot skills from human demonstration
Michael Kaiser and Rüdiger Dillmann · 1996
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The role of internal models in motion planning and control: evidence from grip force adjustments during movements of hand-held loads
J Randall Flanagan and Alan M Wing · 1997
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Internal models in the cerebellum
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Cerebellar complex spikes encode both destinations and errors in arm movements
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Multiple paired forward-inverse models for human motor learning and control
Masahiko Haruno, Daniel M Wolpert, and Mitsuo Kawato · 1998
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Canonical microcircuits for predictive coding
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Lisa Feldman Barrett and W Kyle Simmons · 2015
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Unsupervised learning of spatiotemporally coherent metrics
Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, and Yann LeCun · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Pixel recurrent neural networks
Aäron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
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Loss is its own reward: Self-supervision for reinforcement learning
Evan Shelhamer, Parsa Mahmoudieh, Max Argus, and Trevor Darrell · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A Efros, and Trevor Darrell · 2017
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Time-contrastive networks: Self-supervised learning from video
Pierre Sermanet, Corey Lynch, Yevgen Chebotar, Jasmine Hsu, Eric Jang, Stefan Schaal, Sergey Levine, and Google Brain · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Multiple interactions made easy (mime): Large scale demonstrations data for imitation
Pratyusha Sharma, Lekha Mohan, Lerrel Pinto, and Abhinav Gupta · 2018
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William Whitney, Rajat Agarwal, Kyunghyun Cho, and Abhinav Gupta · 2019
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Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning
Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, and Karol Hausman · 2019
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Self-supervised deep depth denoising
Vladimiros Sterzentsenko, Leonidas Saroglou, Anargyros Chatzitofis, Spyridon Thermos, Nikolaos Zioulis, Alexandros Doumanoglou, Dimitrios Zarpalas, and Petros Daras · 2019
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Selfie: Self-supervised pretraining for image embedding
Trieu H Trinh, Minh-Thang Luong, and Quoc V Le · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Learning correspondence from the cycle-consistency of time
Xiaolong Wang, Allan Jabri, and Alexei A Efros · 2019
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Temporal cycle-consistency learning
Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman · 2019
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Online object representations with contrastive learning
Sören Pirk, Mohi Khansari, Yunfei Bai, Corey Lynch, and Pierre Sermanet · 2019
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Playful interactions for representation learning
Sarah Young, Jyothish Pari, Pieter Abbeel, and Lerrel Pinto · 2022
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Reinforcement learning with action-free pre-training from videos
Younggyo Seo, Kimin Lee, Stephen L James, and Pieter Abbeel · 2022
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Byol-explore: Exploration by bootstrapped prediction
Zhaohan Guo, Shantanu Thakoor, Miruna Pîslar, Bernardo Avila Pires, Florent Altché, Corentin Tallec, Alaa Saade, Daniele Calandriello, Jean-Bastien Grill, Yunhao Tang, et al · 2022
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Human-to-robot imitation in the wild
Shikhar Bahl, Abhinav Gupta, and Deepak Pathak · 2022
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Dexmv: Imitation learning for dexterous manipulation from human videos
Yuzhe Qin, Yueh-Hua Wu, Shaowei Liu, Hanwen Jiang, Ruihan Yang, Yang Fu, and Xiaolong Wang · 2022
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Ashley Edwards, Himanshu Sahni, Yannick Schroecker, and Charles Isbell · 2019
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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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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Data-efficient reinforcement learning with self-predictive representations
Max Schwarzer, Ankesh Anand, Rishab Goel, R Devon Hjelm, Aaron Courville, and Philip Bachman · 2020
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Learning predictive models from observation and interaction
Karl Schmeckpeper, Annie Xie, Oleh Rybkin, Stephen Tian, Kostas Daniilidis, Sergey Levine, and Chelsea Finn · 2020
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Mastering atari, go, chess and shogi by planning with a learned model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, et al · 2020
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Mastering atari with discrete world models
Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, and Jimmy Ba · 2020
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Aravind Sivakumar, Kenneth Shaw, and Deepak Pathak · 2022
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Become a proficient player with limited data through watching pure videos
Weirui Ye, Yunsheng Zhang, Pieter Abbeel, and Yang Gao · 2022
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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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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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An unbiased look at datasets for visuo-motor pre-training
Sudeep Dasari, Mohan Kumar Srirama, Unnat Jain, and Abhinav Gupta · 2023
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Holo-dex: Teaching dexterity with immersive mixed reality
Sridhar Pandian Arunachalam, Irmak Güzey, Soumith Chintala, and Lerrel Pinto · 2023
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Denoising diffusion autoencoders are unified self-supervised learners
Weilai Xiang, Hongyu Yang, Di Huang, and Yunhong Wang · 2023
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Self-supervised learning from images with a joint-embedding predictive architecture
Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, and Nicolas Ballas · 2023
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V-jepa: Latent video prediction for visual representation learning
Adrien Bardes, Quentin Garrido, Jean Ponce, Xinlei Chen, Michael Rabbat, Yann LeCun, Mido Assran, and Nicolas Ballas · 2023
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Goal-conditioned imitation learning using score-based diffusion policies
Moritz Reuss, Maximilian Li, Xiaogang Jia, and Rudolf Lioutikov · 2023
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Learning to act without actions
Dominik Schmidt and Minqi Jiang · 2023
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Open x-embodiment: Robotic learning datasets and rt-x models
Abhishek Padalkar, Acorn Pooley, Ajinkya Jain, Alex Bewley, Alex Herzog, Alex Irpan, Alexander Khazatsky, Anant Rai, Anikait Singh, Anthony Brohan, et al · 2023
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Nur Muhammad Mahi Shafiullah, Anant Rai, Haritheja Etukuru, Yiqian Liu, Ishan Misra, Soumith Chintala, and Lerrel Pinto · 2023
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Train offline, test online: A real robot learning benchmark
Gaoyue Zhou, Victoria Dean, Mohan Kumar Srirama, Aravind Rajeswaran, Jyothish Pari, Kyle Hatch, Aryan Jain, Tianhe Yu, Pieter Abbeel, Lerrel Pinto, et al · 2023
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Dexterity from touch: Self-supervised pre-training of tactile representations with robotic play
Irmak Guzey, Ben Evans, Soumith Chintala, and Lerrel Pinto · 2023
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Behavior generation with latent actions
Seungjae Lee, Yibin Wang, Haritheja Etukuru, H Jin Kim, Nur Muhammad Mahi Shafiullah, and Lerrel Pinto · 2024
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Inverse dynamics pretraining learns good representations for multitask imitation
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Libero: Benchmarking knowledge transfer for lifelong robot learning
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Revisiting feature prediction for learning visual representations from video
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