Fetching the paper…
Reading the bibliography…
A long-standing challenge in AI is to develop agents capable of solving a wide range of physical tasks and generalizing to new, unseen tasks and environments.
Combating the compounding-error problem with a multi-step model
Kavosh Asadi, Dipendra Misra, and Michael L. Littman · 1905
Earlier work this paper cites.
Model predictive control: theory and practice—a survey
C. E. Garcia, D. M. Prett, and M. Morari · 1989
Earlier work this paper cites.
Finding structure in time
Jeffrey L. Elman · 1990
Earlier work this paper cites.
Learning long-term dependencies with gradient descent is difficult
Yoshua Bengio, Patrice Simard, and Paolo Frasconi · 1994
Earlier work this paper cites.
Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation
N. Hansen and A. Ostermeier · 1996
Earlier work this paper cites.
A tutorial on visual servo control
S. Hutchinson, G. Hager, and P. Corke · 1996
Earlier work this paper cites.
Tutorial on training recurrent neural networks, covering bppt, rtrl, ekf and the echo state network approach
Herbert Jaeger · 2002
Earlier work this paper cites.
A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey J. Gordon, and Drew Bagnell · 2011
Earlier work this paper cites.
On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio · 2013
Earlier work this paper cites.
Scheduled sampling for sequence prediction with recurrent neural networks, 2015
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer · 2015
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
Earlier work this paper cites.
Embed to control: A locally linear latent dynamics model for control from raw images
Manuel Watter, Jost Tobias Springenberg, Joschka Boedecker, and Martin Riedmiller · 2015
Earlier work this paper cites.
Model predictive path integral control using covariance variable importance sampling, 2015
Grady Williams, Andrew Aldrich, and Evangelos Theodorou · 2015
Earlier work this paper cites.
Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
Earlier work this paper cites.
Self-correcting models for model-based reinforcement learning
Erik Talvitie · 2016
Earlier work this paper cites.
Predictive Control for Linear and Hybrid Systems
Francesco Borrelli, Alberto Bemporad, and Manfred Morari · 2017
Earlier work this paper cites.
Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Earlier work this paper cites.
Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke van Hoof, and David Meger · 2018
Earlier work this paper cites.
Recurrent world models facilitate policy evolution
David Ha and Jürgen Schmidhuber · 2018
Earlier work this paper cites.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
Earlier work this paper cites.
Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
Earlier work this paper cites.
Universal planning networks: Learning generalizable representations for visuomotor control
Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2018
Earlier work this paper cites.
Lipschitz regularity of deep neural networks: analysis and efficient estimation
Aladin Virmaux and Kevin Scaman · 2018
Earlier work this paper cites.
The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang · 2018
Earlier work this paper cites.
Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
Earlier work this paper cites.
CMA-ES/pycma on Github
Nikolaus Hansen, Youhei Akimoto, and Petr Baudis · 2019
Earlier work this paper cites.
Planning with goal-conditioned policies
Soroush Nasiriany, Vitchyr H. Pong, Steven Lin, and Sergey Levine · 2019
Earlier work this paper cites.
Understanding and improving layer normalization
Jingjing Xu, Xu Sun, Zhiyuan Zhang, Guangxiang Zhao, and Junyang Lin · 2019
Earlier work this paper cites.
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning, 2019
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Avnish Narayan, Hayden Shively, Adithya Bellathur, Karol Hausman, Chelsea Finn, and Sergey Levine · 2019
Earlier work this paper cites.
D4rl: Datasets for deep data-driven reinforcement learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
Earlier work this paper cites.
Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2020
Earlier work this paper cites.
Hierarchical foresight: Self-supervised learning of long-horizon tasks via visual subgoal generation
Suraj Nair and Chelsea Finn · 2020
Cited alongside, same era.
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, Timothy Lillicrap, and David Silver · 2020
Cited alongside, same era.
Planning to explore via self-supervised world models
Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis, Pieter Abbeel, Danijar Hafner, and Deepak Pathak · 2020
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, Yifeng Zhu, and Kevin Lin · 2020
Cited alongside, same era.
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, Afroz Mohiuddin, Ryan Sepassi, George Tucker, and Henryk Michalewski · 2020
Revisiting feature prediction for learning visual representations from video, 2024
Adrien Bardes, Quentin Garrido, Jean Ponce, Xinlei Chen, Michael Rabbat, Yann LeCun, Mido Assran, and Nicolas Ballas · 2024
Later among the works it cites.
π 0 \pi_{0} : A vision-language-action flow model for general robot control, 2024
Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, Szymon Jakubczak, Tim Jones, Liyiming Ke, Sergey Levine, Adrian Li-Bell, Mohith Mothukuri, Suraj Nair, Karl Pertsch, Lucy Xiaoyang Shi, James Tanner, Quan Vuong, Anna Walling, Haohuan Wang, and Ury Zhilinsky · 2024
Later among the works it cites.
Video generation models as world simulators, 2024
Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, et al · 2024
Later among the works it cites.
Genie: Generative interactive environments
Jake Bruce, Michael D Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, et al · 2024
Later among the works it cites.
Vision transformers need registers
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Nevergrad: black-box optimization platform
Pauline Bennet, Carola Doerr, Antoine Moreau, Jeremy Rapin, Fabien Teytaud, and Olivier Teytaud · 2021
Cited alongside, same era.
The mit humanoid robot: Design, motion planning, and control for acrobatic behaviors
Matthew Chignoli, Donghyun Kim, Elijah Stanger-Jones, and Sangbae Kim · 2021
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
Cited alongside, same era.
Mastering atari with discrete world models
Danijar Hafner, Timothy P Lillicrap, Mohammad Norouzi, and Jimmy Ba · 2021
Cited alongside, same era.
Discovering and achieving goals via world models
Russell Mendonca, Oleh Rybkin, Kostas Daniilidis, Danijar Hafner, and Deepak Pathak · 2021
Cited alongside, same era.
Rapid exploration for open-world navigation with latent goal models
Dhruv Shah, Benjamin Eysenbach, Nicholas Rhinehart, and Sergey Levine · 2021
Cited alongside, same era.
Planning to practice: Efficient online fine-tuning by composing goals in latent space
Kuan Fang, Patrick Yin, Ashvin Nair, and Sergey Levine · 2022
Cited alongside, same era.
Timothée Darcet, Maxime Oquab, Julien Mairal, and Piotr Bojanowski · 2024
Later among the works it cites.
Droid: A large-scale in-the-wild robot manipulation dataset, 2024
Alexander Khazatsky et al · 2024
Later among the works it cites.
Learning and leveraging world models in visual representation learning, 2024
Quentin Garrido, Mahmoud Assran, Nicolas Ballas, Adrien Bardes, Laurent Najman, and Yann LeCun · 2024
Later among the works it cites.
Mastering diverse domains through world models, 2024
Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap · 2024
Later among the works it cites.
Td-mpc2: Scalable, robust world models for continuous control
Nicklas Hansen, Hao Su, and Xiaolong Wang · 2024
Later among the works it cites.
Robocasa: Large-scale simulation of everyday tasks for generalist robots
Soroush Nasiriany, Abhiram Maddukuri, Lance Zhang, Adeet Parikh, Aaron Lo, Abhishek Joshi, Ajay Mandlekar, and Yuke Zhu · 2024
Later among the works it cites.
Octo: An open-source generalist robot policy
Octo Model Team, Dibya Ghosh, Homer Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, Charles Xu, Jianlan Luo, Tobias Kreiman, You Liang Tan, Lawrence Yunliang Chen, Pannag Sanketi, Quan Vuong, Ted Xiao, Dorsa Sadigh, Chelsea Finn, and Sergey Levine · 2024
Later among the works it cites.
Foundation policies with hilbert representations
Seohong Park, Tobias Kreiman, and Sergey Levine · 2024
Later among the works it cites.
Genie 2: A large-scale foundation world model
Jack Parker-Holder, Philip Ball, Jake Bruce, Vibhavari Dasagi, Kristian Holsheimer, Christos Kaplanis, Alexandre Moufarek, Guy Scully, Jeremy Shar, Jimmy Shi, Stephen Spencer, Jessica Yung, Michael Dennis, Sultan Kenjeyev, Shangbang Long, Vlad Mnih, Harris Chan, Maxime Gazeau, Bonnie Li, Fabio Pardo, Luyu Wang, Lei Zhang, Frederic Besse, Tim Harley, Anna Mitenkova, Jane Wang, Jeff Clune, Demis Hassabis, Raia Hadsell, Adrian Bolton, Satinder Singh, and Tim Rocktäschel · 2024
Later among the works it cites.
Roformer: Enhanced transformer with rotary position embedding
Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu · 2024
Later among the works it cites.
Cosmos world foundation model platform for physical ai
Niket Agarwal, Arslan Ali, Maciej Bala, Yogesh Balaji, Erik Barker, Tiffany Cai, Prithvijit Chattopadhyay, Yongxin Chen, Yin Cui, Yifan Ding, et al · 2025
Closest in time.
V-jepa 2: Self-supervised video models enable understanding, prediction and planning, 2025
Mido Assran, Adrien Bardes, David Fan, Quentin Garrido, Russell Howes, Mojtaba, Komeili, Matthew Muckley, Ammar Rizvi, Claire Roberts, Koustuv Sinha, Artem Zholus, Sergio Arnaud, Abha Gejji, Ada Martin, Francois Robert Hogan, Daniel Dugas, Piotr Bojanowski, Vasil Khalidov, Patrick Labatut, Francisco Massa, Marc Szafraniec, Kapil Krishnakumar, Yong Li, Xiaodong Ma, Sarath Chandar, Franziska Meier, Yann LeCun, Michael Rabbat, and Nicolas Ballas · 2025
Closest in time.
Back to the features: Dino as a foundation for video world models, 2025
Federico Baldassarre, Marc Szafraniec, Basile Terver, Vasil Khalidov, Francisco Massa, Yann LeCun, Patrick Labatut, Maximilian Seitzer, and Piotr Bojanowski · 2025
Closest in time.
Genie 3: A new frontier for world models
Philip J. Ball, Jakob Bauer, Frank Belletti, Bethanie Brownfield, Ariel Ephrat, Shlomi Fruchter, Agrim Gupta, Kristian Holsheimer, Aleksander Holynski, Jiri Hron, Christos Kaplanis, Marjorie Limont, Matt McGill, Yanko Oliveira, Jack Parker-Holder, Frank Perbet, Guy Scully, Jeremy Shar, Stephen Spencer, Omer Tov, Ruben Villegas, Emma Wang, Jessica Yung, Cip Baetu, Jordi Berbel, David Bridson, Jake Bruce, Gavin Buttimore, Sarah Chakera, Bilva Chandra, Paul Collins, Alex Cullum, Bogdan Damoc, Vibha Dasagi, Maxime Gazeau, Charles Gbadamosi, Woohyun Han, Ed Hirst, Ashyana Kachra, Lucie Kerley, Kristian Kjems, Eva Knoepfel, Vika Koriakin, Jessica Lo, Cong Lu, Zeb Mehring, Alex Moufarek, Henna Nandwani, Valeria Oliveira, Fabio Pardo, Jane Park, Andrew Pierson, Ben Poole, Helen Ran, Tim Salimans, Manuel Sanchez, Igor Saprykin, Amy Shen, Sailesh Sidhwani, Duncan Smith, Joe Stanton, Hamish Tomlinson, Dimple Vijaykumar, Luyu Wang, Piers Wingfield, Nat Wong, Keyang Xu, Christopher Yew, Nick Young, Vadim Zubov, Douglas Eck, Dumitru Erhan, Koray Kavukcuoglu, Demis Hassabis, Zoubin Gharamani, Raia Hadsell, Aäron van den Oord, Inbar Mosseri, Adrian Bolton, Satinder Singh, and Tim Rocktäschel · 2025
Closest in time.
Navigation world models
Amir Bar, Gaoyue Zhou, Danny Tran, Trevor Darrell, and Yann LeCun · 2025
Closest in time.
Vavim and vavam: Autonomous driving through video generative modeling
Florent Bartoccioni, Elias Ramzi, Victor Besnier, Shashanka Venkataramanan, Tuan-Hung Vu, Yihong Xu, Loick Chambon, Spyros Gidaris, Serkan Odabas, David Hurych, Renaud Marlet, Alexandre Boulch, Mickael Chen, Éloi Zablocki, Andrei Bursuc, Eduardo Valle, and Matthieu Cord · 2025
Closest in time.
Embodied ai agents: Modeling the world, 2025
Pascale Fung, Yoram Bachrach, Asli Celikyilmaz, Kamalika Chaudhuri, Delong Chen, Willy Chung, Emmanuel Dupoux, Hongyu Gong, Hervé Jégou, Alessandro Lazaric, Arjun Majumdar, Andrea Madotto, Franziska Meier, Florian Metze, Louis-Philippe Morency, Théo Moutakanni, Juan Pino, Basile Terver, Joseph Tighe, Paden Tomasello, and Jitendra Malik · 2025
Closest in time.
Offline goal-conditioned reinforcement learning with quasimetric representations
Vivek Myers, Bill Zheng, Benjamin Eysenbach, and Sergey Levine · 2025
Closest in time.
OGBench: Benchmarking offline goal-conditioned RL
Seohong Park, Kevin Frans, Benjamin Eysenbach, and Sergey Levine · 2025
Closest in time.
Closing the train-test gap in world models for gradient-based planning, 2025
Arjun Parthasarathy, Nimit Kalra, Rohun Agrawal, Yann LeCun, Oumayma Bounou, Pavel Izmailov, and Micah Goldblum · 2025
Closest in time.
Oriane Siméoni, Huy V. Vo, Maximilian Seitzer, Federico Baldassarre, Maxime Oquab, Cijo Jose, Vasil Khalidov, Marc Szafraniec, Seungeun Yi, Michaël Ramamonjisoa, Francisco Massa, Daniel Haziza, Luca Wehrstedt, Jianyuan Wang, Timothée Darcet, Théo Moutakanni, Leonel Sentana, Claire Roberts, Andrea Vedaldi, Jamie Tolan, John Brandt, Camille Couprie, Julien Mairal, Hervé Jégou, Patrick Labatut, and Piotr Bojanowski · 2025
Closest in time.
Learning from reward-free offline data: A case for planning with latent dynamics models, 02 2025
Vlad Sobal, Wancong Zhang, Kynghyun Cho, Randall Balestriero, Tim Rudner, and Yann Lecun · 2025
Closest in time.
TD-JEPA: Latent-predictive representations for zero-shot reinforcement learning
Marco Bagatella, Matteo Pirotta, Ahmed Touati, Alessandro Lazaric, and Andrea Tirinzoni · 2026
Closest in time.
A lightweight library for energy-based joint-embedding predictive architectures, 2026
Basile Terver, Randall Balestriero, Megi Dervishi, David Fan, Quentin Garrido, Tushar Nagarajan, Koustuv Sinha, Wancong Zhang, Mike Rabbat, Yann LeCun, and Amir Bar · 2026
Closest in time.
Leonardo F. Toso, Davit Shadunts, Yunyang Lu, Nihal Sharma, Donglin Zhan, Nam H. Nguyen, and James Anderson · 2026
Closest in time.
Ddp-wm: Disentangled dynamics prediction for efficient world models, 2026
Shicheng Yin, Kaixuan Yin, Weixing Chen, Yang Liu, Guanbin Li, and Liang Lin · 2026
Closest in time.
Sparse world models: Visual world modeling with sparse representations, 2026
Qilong Zhao, Fan Feng, Housheng Hai, and Biwei Huang · 2026
Closest in time.