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A world model provides an agent with a representation of its environment, enabling it to predict the causal consequences of its actions.
Optimal input signals for parameter estimation in dynamic systems–survey and new results
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Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2019
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Contrastive learning of structured world models
Thomas Kipf, Elise Van der Pol, and Max Welling · 2019
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Closing the sim-to-real loop: Adapting simulation randomization with real world experience
Yevgen Chebotar, Ankur Handa, Viktor Makoviychuk, Miles Macklin, Jan Issac, Nathan Ratliff, and Dieter Fox · 2019
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Mastering atari with discrete world models
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Reinforcement learning with augmented data
Misha Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, and Aravind Srinivas · 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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Counterfactual data augmentation using locally factored dynamics
Silviu Pitis, Elliot Creager, and Animesh Garg · 2020
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Rlbench: The robot learning benchmark & learning environment
Stephen James, Zicong Ma, David Rovick Arrojo, and Andrew J Davison · 2020
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Transporter networks: Rearranging the visual world for robotic manipulation
Andy Zeng, Pete Florence, Jonathan Tompson, Stefan Welker, Jonathan Chien, Maria Attarian, Travis Armstrong, Ivan Krasin, Dan Duong, Vikas Sindhwani, et al · 2021
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Self-supervised visual reinforcement learning with object-centric representations
Andrii Zadaianchuk, Maximilian Seitzer, and Georg Martius · 2021
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Coarse-to-fine q-attention: Efficient learning for visual robotic manipulation via discretisation
Stephen James, Kentaro Wada, Tristan Laidlow, and Andrew J Davison · 2022
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Cliport: What and where pathways for robotic manipulation
Mohit Shridhar, Lucas Manuelli, and Dieter Fox · 2022
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Mocoda: Model-based counterfactual data augmentation
Silviu Pitis, Elliot Creager, Ajay Mandlekar, 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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Estimating the center of mass of an unknown object for nonprehensile manipulation
Ziyan Gao, Armagan Elibol, and Nak Young Chong · 2022
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Learning interactive real-world simulators
Mengjiao Yang, Yilun Du, Kamyar Ghasemipour, Jonathan Tompson, Dale Schuurmans, and Pieter Abbeel · 2023
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3d gaussian splatting for real-time radiance field rendering
Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis · 2023
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Mimicgen: A data generation system for scalable robot learning using human demonstrations
Ajay Mandlekar, Soroush Nasiriany, Bowen Wen, Iretiayo Akinola, Yashraj Narang, Linxi Fan, Yuke Zhu, and Dieter Fox · 2023
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Mastering diverse domains through world models
Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap · 2023
Sam 2: Segment anything in images and videos
Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, et al · 2024
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Grounded sam: Assembling open-world models for diverse visual tasks
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Genie: Generative interactive environments
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Physgaussian: Physics-integrated 3d gaussians for generative dynamics
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Adriver-i: A general world model for autonomous driving
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Gaia-1: A generative world model for autonomous driving
Anthony Hu, Lloyd Russell, Hudson Yeo, Zak Murez, George Fedoseev, Alex Kendall, Jamie Shotton, and Gianluca Corrado · 2023
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Daydreamer: World models for physical robot learning
Philipp Wu, Alejandro Escontrela, Danijar Hafner, Pieter Abbeel, and Ken Goldberg · 2023
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PAC-neRF: Physics augmented continuum neural radiance fields for geometry-agnostic system identification
Xuan Li, Yi-Ling Qiao, Peter Yichen Chen, Krishna Murthy Jatavallabhula, Ming Lin, Chenfanfu Jiang, and Chuang Gan · 2023
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Segment anything
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
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Tracking anything with decoupled video segmentation
Ho Kei Cheng, Seoung Wug Oh, Brian Price, Alexander Schwing, and Joon-Young Lee · 2023
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Perceiver-actor: A multi-task transformer for robotic manipulation
Mohit Shridhar, Lucas Manuelli, and Dieter Fox · 2023
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Lukas Meyer, Floris Erich, Yusuke Yoshiyasu, Marc Stamminger, Noriaki Ando, and Yukiyasu Domae · 2024
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Robo-gs: A physics consistent spatial-temporal model for robotic arm with hybrid representation
Haozhe Lou, Yurong Liu, Yike Pan, Yiran Geng, Jianteng Chen, Wenlong Ma, Chenglong Li, Lin Wang, Hengzhen Feng, Lu Shi, et al · 2024
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Physically embodied gaussian splatting: A realtime correctable world model for robotics
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Primp: Probabilistically-informed motion primitives for efficient affordance learning from demonstration
Sipu Ruan, Weixiao Liu, Xiaoli Wang, Xin Meng, and Gregory S Chirikjian · 2024
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Reconciling reality through simulation: A real-to-sim-to-real approach for robust manipulation
Marcel Torne, Anthony Simeonov, Zechu Li, April Chan, Tao Chen, Abhishek Gupta, and Pulkit Agrawal · 2024
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Learning 3d particle-based simulators from RGB-d videos
William F Whitney, Tatiana Lopez-Guevara, Tobias Pfaff, Yulia Rubanova, Thomas Kipf, Kim Stachenfeld, and Kelsey R Allen · 2024
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Rvt-2: Learning precise manipulation from few demonstrations
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Understanding when dynamics-invariant data augmentations benefit model-free reinforcement learning updates
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2d gaussian splatting for geometrically accurate radiance fields
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Neural assets: 3d-aware multi-object scene synthesis with image diffusion models
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Zero-shot object-centric representation learning
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Object-centric learning for real-world videos by predicting temporal feature similarities
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The devil is in the fine-grained details: Evaluating open-vocabulary object detectors for fine-grained understanding
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Scaling robot-learning by crowdsourcing simulation environments
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Multi-camera hand-eye calibration for human-robot collaboration in industrial robotic workcells
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Causal action influence aware counterfactual data augmentation
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