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World models play a crucial role in decision-making within embodied environments, enabling cost-free explorations that would otherwise be expensive in the real world.
Integrated architectures for learning, planning, and reacting based on approximating dynamic programming
Richard S Sutton · 1990
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Markov decision processes
Martin L Puterman · 1990
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Dyna, an integrated architecture for learning, planning, and reacting
Richard S Sutton · 1991
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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Scope of validity of psnr in image/video quality assessment
Quan Huynh-Thu and Mohammed Ghanbari · 2008
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Motion-dependent representation of space in area mt+
Gerrit W Maus, Jason Fischer, and David Whitney · 2013
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Markov decision processes: discrete stochastic dynamic programming
Martin L Puterman · 2014
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Primary visual cortex represents the difference between past and present
Nora Nortmann, Sascha Rekauzke, Selim Onat, Peter König, and Dirk Jancke · 2015
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Action-conditional video prediction using deep networks in atari games
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L Lewis, and Satinder Singh · 2015
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beta-VAE: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
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David Ha and Jürgen Schmidhuber · 2018
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Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Towards accurate generative models of video: A new metric & challenges
Thomas Unterthiner, Sjoerd Van Steenkiste, Karol Kurach, Raphael Marinier, Marcin Michalski, and Sylvain Gelly · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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QT-Opt: Scalable deep reinforcement learning for vision-based robotic manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, et al · 2018
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RoboTurk: A crowdsourcing platform for robotic skill learning through imitation
Ajay Mandlekar, Yuke Zhu, Animesh Garg, Jonathan Booher, Max Spero, Albert Tung, Julian Gao, John Emmons, Anchit Gupta, Emre Orbay, Silvio Savarese, and Li Fei-Fei · 2018
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Algorithmic framework for model-based deep reinforcement learning with theoretical guarantees
Yuping Luo, Huazhe Xu, Yuanzhi Li, Yuandong Tian, Trevor Darrell, and Tengyu Ma · 2019
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When to trust your model: Model-based policy optimization
Michael Janner, Justin Fu, Marvin Zhang, and Sergey Levine · 2019
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High fidelity video prediction with large stochastic recurrent neural networks
Ruben Villegas, Arkanath Pathak, Harini Kannan, Dumitru Erhan, Quoc V Le, and Honglak Lee · 2019
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Combating the compounding-error problem with a multi-step model
Kavosh Asadi, Dipendra Misra, Seungchan Kim, and Michel L Littman · 2019
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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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MOPO: model-based offline policy optimization
Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y. Zou, Sergey Levine, Chelsea Finn, and Tengyu Ma · 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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Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 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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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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Morel: Model-based offline reinforcement learning
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, and Thorsten Joachims · 2020
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Mopo: Model-based offline policy optimization
Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y Zou, Sergey Levine, Chelsea Finn, and Tengyu Ma · 2020
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Shared Control Templates for Assistive Robotics
Gabriel Quere, Annette Hagengruber, Maged Iskandar, Samuel Bustamante, Daniel Leidner, Freek Stulp, and Joern Vogel · 2020
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Benchmarks for deep off-policy evaluation
Justin Fu, Mohammad Norouzi, Ofir Nachum, George Tucker, Ziyu Wang, Alexander Novikov, Mengjiao Yang, Michael R. Zhang, Yutian Chen, Aviral Kumar, Cosmin Paduraru, Sergey Levine, and Tom Le Paine · 2021
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COMBO: conservative offline model-based policy optimization
Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, and Chelsea Finn · 2021
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Mastering atari with discrete world models
Danijar Hafner, Timothy P Lillicrap, Mohammad Norouzi, and Jimmy Ba · 2021
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Fitvid: Overfitting in pixel-level video prediction
Mohammad Babaeizadeh, Mohammad Taghi Saffar, Suraj Nair, Sergey Levine, Chelsea Finn, and Dumitru Erhan · 2021
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Is pessimism provably efficient for offline rl?
Ying Jin, Zhuoran Yang, and Zhaoran Wang · 2021
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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A control-centric benchmark for video prediction
Stephen Tian, Chelsea Finn, and Jiajun Wu · 2023
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Bridgedata v2: A dataset for robot learning at scale, 2023
Homer Walke, Kevin Black, Abraham Lee, Moo Jin Kim, Max Du, Chongyi Zheng, Tony Zhao, Philippe Hansen-Estruch, Quan Vuong, Andre He, Vivek Myers, Kuan Fang, Chelsea Finn, and Sergey Levine · 2023
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Grounding language with visual affordances over unstructured data
Oier Mees, Jessica Borja-Diaz, and Wolfram Burgard · 2023
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CLVR jaco play dataset, 2023
Shivin Dass, Jullian Yapeter, Jesse Zhang, Jiahui Zhang, Karl Pertsch, Stefanos Nikolaidis, and Joseph J. Lim · 2023
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Offline reinforcement learning from images with latent space models
Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, and Chelsea Finn · 2021
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OPAL: offline primitive discovery for accelerating offline reinforcement learning
Anurag Ajay, Aviral Kumar, Pulkit Agrawal, Sergey Levine, and Ofir Nachum · 2021
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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
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Rambo-rl: Robust adversarial model-based offline reinforcement learning
Marc Rigter, Bruno Lacerda, and Nick Hawes · 2022
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Temporal difference learning for model predictive control
Nicklas A Hansen, Hao Su, and Xiaolong Wang · 2022
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Mcvd-masked conditional video diffusion for prediction, generation, and interpolation
Vikram Voleti, Alexia Jolicoeur-Martineau, and Chris Pal · 2022
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Error bounds of imitating policies and environments for reinforcement learning
Tian Xu, Ziniu Li, and Yang Yu · 2022
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Multi-stage cable routing through hierarchical imitation learning
Jianlan Luo, Charles Xu, Xinyang Geng, Gilbert Feng, Kuan Fang, Liam Tan, Stefan Schaal, and Sergey Levine · 2023
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Viola: Imitation learning for vision-based manipulation with object proposal priors, 2023
Yifeng Zhu, Abhishek Joshi, Peter Stone, and Yuke Zhu · 2023
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Train offline, test online: A real robot learning benchmark, 2023
Gaoyue Zhou, Victoria Dean, Mohan Kumar Srirama, Aravind Rajeswaran, Jyothish Pari, Kyle Hatch, Aryan Jain, Tianhe Yu, Pieter Abbeel, Lerrel Pinto, Chelsea Finn, and Abhinav Gupta · 2023
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Interactive language: Talking to robots in real time
Corey Lynch, Ayzaan Wahid, Jonathan Tompson, Tianli Ding, James Betker, Robert Baruch, Travis Armstrong, and Pete Florence · 2023
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Hydra: Hybrid robot actions for imitation learning
Suneel Belkhale, Yuchen Cui, 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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ucsd kitchens Dataset
Ge Yan, Kris Wu, and Xiaolong Wang · 2023
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Robot learning on the job: Human-in-the-loop autonomy and learning during deployment
Huihan Liu, Soroush Nasiriany, Lance Zhang, Zhiyao Bao, and Yuke Zhu · 2023
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Multi-resolution sensing for real-time control with vision-language models
Saumya Saxena, Mohit Sharma, and Oliver Kroemer · 2023
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MUTEX: Learning unified policies from multimodal task specifications
Rutav Shah, Roberto Martín-Martín, and Yuke Zhu · 2023
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Fanuc manipulation: A dataset for learning-based manipulation with fanuc mate 200id robot
Xinghao Zhu, Ran Tian, Chenfeng Xu, Mingyu Ding, Wei Zhan, and Masayoshi Tomizuka · 2023
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Structured world models from human videos
Russell Mendonca, Shikhar Bahl, and Deepak Pathak · 2023
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On bringing robots home, 2023
Nur Muhammad Mahi Shafiullah, Anant Rai, Haritheja Etukuru, Yiqian Liu, Ishan Misra, Soumith Chintala, and Lerrel Pinto · 2023
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Policy-conditioned environment models are more generalizable
Ruifeng Chen, Xiong-Hui Chen, Yihao Sun, Siyuan Xiao, Minhui Li, and Yang Yu · 2024
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Spatial-temporal transformer networks for traffic flow forecasting using a pre-trained language model
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iVideoGPT: Interactive VideoGPTs are scalable world models
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Any-step dynamics model improves future predictions for online and offline reinforcement learning
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Reward-consistent dynamics models are strongly generalizable for offline reinforcement learning
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Offline transition modeling via contrastive energy learning
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Reasoning with latent diffusion in offline reinforcement learning
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Droid: A large-scale in-the-wild robot manipulation dataset
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