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Manipulating objects is a hallmark of human intelligence, and an important task in domains such as robotics.
Learning to predict by the methods of temporal differences
Richard S Sutton · 1988
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Neural machine translation by jointly learning to align and translate
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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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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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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
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Addressing function approximation error in actor-critic methods
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Unsupervised learning of object landmarks through conditional image generation
Tomas Jakab, Ankush Gupta, Hakan Bilen, and Andrea Vedaldi · 2018
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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, and Sergey Levine · 2018
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Visual reinforcement learning with imagined goals
Ashvin V Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, and Sergey Levine · 2018
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Graph networks as learnable physics engines for inference and control
Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, and Peter Battaglia · 2018
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Multi-object representation learning with iterative variational inference
Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Christopher Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, and Alexander Lerchner · 2019
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Structured object-aware physics prediction for video modeling and planning
Jannik Kossen, Karl Stelzner, Marcel Hussing, Claas Voelcker, and Kristian Kersting · 2019
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Skew-fit: State-covering self-supervised reinforcement learning
Vitchyr H Pong, Murtaza Dalal, Steven Lin, Ashvin Nair, Shikhar Bahl, and Sergey Levine · 2019
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Nicholas Watters, Loic Matthey, Matko Bosnjak, Christopher P Burgess, and Alexander Lerchner · 2019
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Illiterate dall-e learns to compose
Gautam Singh, Sungjin Ahn, and Fei Deng · 2022
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Learning what and where: Disentangling location and identity tracking without supervision
Manuel Traub, Sebastian Otte, Tobias Menge, Matthias Karlbauer, Jannik Thuemmel, and Martin V Butz · 2022
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Object-based active inference
Ruben S van Bergen and Pablo Lanillos · 2022
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Slotformer: Unsupervised visual dynamics simulation with object-centric models
Ziyi Wu, Nikita Dvornik, Klaus Greff, Thomas Kipf, and Animesh Garg · 2022
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Self-supervised reinforcement learning with independently controllable subgoals
Andrii Zadaianchuk, Georg Martius, and Fanny Yang · 2022
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Toward compositional generalization in object-oriented world modeling
Linfeng Zhao, Lingzhi Kong, Robin Walters, and Lawson LS Wong · 2022
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Jindong Jiang, Sepehr Janghorbani, Gerard de Melo, and Sungjin Ahn · 2020
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Towards practical multi-object manipulation using relational reinforcement learning
Richard Li, Allan Jabri, Trevor Darrell, and Pulkit Agrawal · 2020
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Object-centric learning with slot attention
Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf · 2020
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Self-supervised visual reinforcement learning with object-centric representations
Andrii Zadaianchuk, Maximilian Seitzer, and Georg Martius · 2020
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https://github.com/karpathy/minGPT , 2021
Andrej Karpathy · 2021
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Isaac gym: High performance gpu-based physics simulation for robot learning, 2021
Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, and Gavriel State · 2021
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Policy architectures for compositional generalization in control
Allan Zhou, Vikash Kumar, Chelsea Finn, and Aravind Rajeswaran · 2022
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Hierarchical abstraction for combinatorial generalization in object rearrangement
Michael Chang, Alyssa Li Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, and Amy Zhang · 2023
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DDLP: Unsupervised object-centric video prediction with deep dynamic latent particles
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Learning multi-object dynamics with compositional neural radiance fields
Danny Driess, Zhiao Huang, Yunzhu Li, Russ Tedrake, and Marc Toussaint · 2023
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Learning dynamic attribute-factored world models for efficient multi-object reinforcement learning
Fan Feng and Sara Magliacane · 2023
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Focus: Object-centric world models for robotics manipulation
Stefano Ferraro, Pietro Mazzaglia, Tim Verbelen, and Bart Dhoedt · 2023
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Efficient RL via disentangled environment and agent representations
Kevin Gmelin, Shikhar Bahl, Russell Mendonca, and Deepak Pathak · 2023
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Mastering diverse domains through world models
Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap · 2023
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Visuomotor control in multi-object scenes using object-aware representations
Negin Heravi, Ayzaan Wahid, Corey Lynch, Pete Florence, Travis Armstrong, Jonathan Tompson, Pierre Sermanet, Jeannette Bohg, and Debidatta Dwibedi · 2023
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A survey on compositional generalization in applications
Baihan Lin, Djallel Bouneffouf, and Irina Rish · 2023
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Structure in reinforcement learning: A survey and open problems
Aditya Mohan, Amy Zhang, and Marius Lindauer · 2023
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An investigation into pre-training object-centric representations for reinforcement learning
Jaesik Yoon, Yi-Fu Wu, Heechul Bae, and Sungjin Ahn · 2023
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