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Visual imitation learning enables robotic agents to acquire skills by observing expert demonstration videos.
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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
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Internal models in the cerebellum
Daniel M Wolpert, R Chris Miall, and Mitsuo Kawato · 1998
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Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y Ng · 2004
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From visuo-motor interactions to imitation learning: behavioural and brain imaging studies
Stefan Vogt and Roland Thomaschke · 2007
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Soft-dtw: a differentiable loss function for time-series
Marco Cuturi and Mathieu Blondel · 2017
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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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Deep visual foresight for planning robot motion
Chelsea Finn and Sergey Levine · 2017
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One-shot visual imitation learning via meta-learning
Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter 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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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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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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Recurrent world models facilitate policy evolution
David Ha and Jürgen Schmidhuber · 2018
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Zero-shot visual imitation
Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Yide Shentu, Evan Shelhamer, Jitendra Malik, Alexei A Efros, and Trevor Darrell · 2018
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Reinforcement Learning: An Introduction
Richard S. Sutton and Andrew G. Barto · 2018
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One-shot imitation from observing humans via domain-adaptive meta-learning
Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Tianhao Zhang, Pieter Abbeel, and Sergey Levine · 2018
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Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
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Pytorch: an imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Super-convergence: Very fast training of neural networks using large learning rates
Leslie N Smith and Nicholay Topin · 2019
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Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2020
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Model based reinforcement learning for atari
Łukasz Kaiser, Mohammad Babaeizadeh, Piotr Miłos, Błażej Osiński, Roy H Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, et al · 2020
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Stochastic latent actor-critic: Deep reinforcement learning with a latent variable model
Alex X Lee, Anusha Nagabandi, Pieter Abbeel, and Sergey Levine · 2020
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Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine · 2020
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Transformers for one-shot visual imitation
Sudeep Dasari and Abhinav Gupta · 2021
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Mastering atari with discrete world models
Transformer-based world models are happy with 100k interactions
Jan Robine, Marc Höftmann, Tobias Uelwer, and Stefan Harmeling · 2023
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Robotic world models—conceptualization, review, and engineering best practices
Ryo Sakagami, Florian S Lay, Andreas Dömel, Martin J Schuster, Alin Albu-Schäffer, and Freek Stulp · 2023
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Mimicplay: Long-horizon imitation learning by watching human play
Chen Wang, Linxi Fan, Jiankai Sun, Ruohan Zhang, Li Fei-Fei, Danfei Xu, Yuke Zhu, and Anima Anandkumar · 2023
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Xskill: Cross embodiment skill discovery
Mengda Xu, Zhenjia Xu, Cheng Chi, Manuela Veloso, and Shuran Song · 2023
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Dynamo: In-domain dynamics pretraining for visuo-motor control
Zichen Jeff Cui, Hengkai Pan, Aadhithya Iyer, Siddhant Haldar, and Lerrel Pinto · 2024
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Dinobot: Robot manipulation via retrieval and alignment with vision foundation models
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Danijar Hafner, Timothy P Lillicrap, Mohammad Norouzi, and Jimmy Ba · 2021
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Discovering and achieving goals via world models
Russell Mendonca, Oleh Rybkin, Kostas Daniilidis, Danijar Hafner, and Deepak Pathak · 2021
Cited alongside, same era.
Attentive one-shot meta-imitation learning from visual demonstration
Vishal Bhutani, Anima Majumder, Madhu Vankadari, Samrat Dutta, Aaditya Asati, and Swagat Kumar · 2022
Cited alongside, same era.
Transdreamer: Reinforcement learning with transformer world models
Chang Chen, Yi-Fu Wu, Jaesik Yoon, and Sungjin Ahn · 2022
Cited alongside, same era.
Accelerate: Training and inference at scale made simple, efficient and adaptable
Sylvain Gugger, Lysandre Debut, Thomas Wolf, Philipp Schmid, Zachary Mueller, Sourab Mangrulkar, Marc Sun, and Benjamin Bossan · 2022
Cited alongside, same era.
Temporal difference learning for model predictive control
Nicklas A Hansen, Hao Su, and Xiaolong Wang · 2022
Cited alongside, same era.
Bc-z: Zero-shot task generalization with robotic imitation learning
Eric Jang, Alex Irpan, Mohi Khansari, Daniel Kappler, Frederik Ebert, Corey Lynch, Sergey Levine, and Chelsea Finn · 2022
Cited alongside, same era.
Norman Di Palo and Edward Johns · 2024
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Learning by watching: A review of video-based learning approaches for robot manipulation
Chrisantus Eze and Christopher Crick · 2024
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Baku: An efficient transformer for multi-task policy learning
Siddhant Haldar, Zhuoran Peng, and Lerrel Pinto · 2024
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Td-mpc2: Scalable, robust world models for continuous control
Nicklas Hansen, Hao Su, and Xiaolong Wang · 2024
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One-shot imitation under mismatched execution
Kushal Kedia, Prithwish Dan, Angela Chao, Maximus Adrian Pace, and Sanjiban Choudhury · 2024
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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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Towards generalist robot learning from internet video: A survey
Robert McCarthy, Daniel CH Tan, Dominik Schmidt, Fernando Acero, Nathan Herr, Yilun Du, Thomas G Thuruthel, and Zhibin Li · 2024
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Dinov2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al · 2024
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Unleashing large-scale video generative pre-training for visual robot manipulation
Hongtao Wu, Ya Jing, Chilam Cheang, Guangzeng Chen, Jiafeng Xu, Xinghang Li, Minghuan Liu, Hang Li, and Tao Kong · 2024
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Autonomous interactive correction mllm for robust robotic manipulation
Chuyan Xiong, Chengyu Shen, Xiaoqi Li, Kaichen Zhou, Jiaming Liu, Ruiping Wang, and Hao Dong · 2024
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Spatiotemporal predictive pre-training for robotic motor control
Jiange Yang, Bei Liu, Jianlong Fu, Bocheng Pan, Gangshan Wu, and Limin Wang · 2024
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A survey of imitation learning: Algorithms, recent developments, and challenges
Maryam Zare, Parham M Kebria, Abbas Khosravi, and Saeid Nahavandi · 2024
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One-shot imitation learning with invariance matching for robotic manipulation
Xinyu Zhang and Abdeslam Boularias · 2024
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Dino-wm: World models on pre-trained visual features enable zero-shot planning
Gaoyue Zhou, Hengkai Pan, Yann LeCun, and Lerrel Pinto · 2024
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Gr00t n1: An open foundation model for generalist humanoid robots
Johan Bjorck, Fernando Castañeda, Nikita Cherniadev, Xingye Da, Runyu Ding, Linxi Fan, Yu Fang, Dieter Fox, Fengyuan Hu, Spencer Huang, et al · 2025
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Haichao Liu, Sikai Guo, Pengfei Mai, Jiahang Cao, Haoang Li, and Jun Ma · 2025
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