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Model-free control strategies such as reinforcement learning have shown the ability to learn control strategies without requiring an accurate model or simulator of the world.
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Optimal experiment design for open and closed-loop system identification
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Mujoco: A physics engine for model-based control
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Fereshteh Sadeghi and Sergey Levine · 2016
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Domain randomization for transferring deep neural networks from simulation to the real world
Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
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Information theoretic mpc for model-based reinforcement learning
Grady Williams, Nolan Wagener, Brian Goldfain, Paul Drews, James M Rehg, Byron Boots, and Evangelos A Theodorou · 2017
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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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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning
Anusha Nagabandi, Gregory Kahn, Ronald S Fearing, and Sergey Levine · 2018
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Learning dexterous in-hand manipulation
Marcin Andrychowicz OpenAI, Bowen Baker, Maciek Chociej, Rafal Józefowicz, Bob McGrew, Jakub W Pachocki, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, et al · 2018
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Fast model identification via physics engines for data-efficient policy search
Shaojun Zhu, Andrew Kimmel, Kostas Bekris, and Abdeslam Boularias · 2018
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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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Adaptive-control-oriented meta-learning for nonlinear systems
SM Richards, N Azizan, J-JE Slotine, and M Pavone · 2021
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Task-optimal exploration in linear dynamical systems
Andrew J Wagenmaker, Max Simchowitz, and Kevin Jamieson · 2021
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Visual dexterity: In-hand dexterous manipulation from depth
Tao Chen, Megha Tippur, Siyang Wu, Vikash Kumar, Edward Adelson, and Pulkit Agrawal · 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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Dextreme: Transfer of agile in-hand manipulation from simulation to reality
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Learning agile and dynamic motor skills for legged robots
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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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Estimating mass distribution of articulated objects using non-prehensile manipulation
K Niranjan Kumar, Irfan Essa, Sehoon Ha, and C Karen Liu · 2019
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Assessing transferability from simulation to reality for reinforcement learning
Fabio Muratore, Michael Gienger, and Jan Peters · 2019
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Self-supervised exploration via disagreement
Deepak Pathak, Dhiraj Gandhi, and Abhinav Gupta · 2019
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Fabio Ramos, Rafael Carvalhaes Possas, and Dieter Fox · 2019
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Model-based active exploration
Pranav Shyam, Wojciech Jaśkowski, and Faustino Gomez · 2019
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Planning with diffusion for flexible behavior synthesis
Michael Janner, Yilun Du, Joshua B Tenenbaum, and Sergey Levine · 2022
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Ditto: Building digital twins of articulated objects from interaction
Zhenyu Jiang, Cheng-Chun Hsu, and Yuke Zhu · 2022
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Active learning for nonlinear system identification with guarantees
Horia Mania, Michael I Jordan, and Benjamin Recht · 2022
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Dimensionality reduction and prioritized exploration for policy search
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Structure from action: Learning interactions for articulated object 3d structure discovery
Neil Nie, Samir Yitzhak Gadre, Kiana Ehsani, and Shuran Song · 2022
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Learning to walk in minutes using massively parallel deep reinforcement learning
Nikita Rudin, David Hoeller, Philipp Reist, and Marco Hutter · 2022
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Adaptive robust model predictive control with matched and unmatched uncertainty
Rohan Sinha, James Harrison, Spencer M Richards, and Marco Pavone · 2022
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Legged robots that keep on learning: Fine-tuning locomotion policies in the real world
Laura Smith, J Chase Kew, Xue Bin Peng, Sehoon Ha, Jie Tan, and Sergey Levine · 2022
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Adaafford: Learning to adapt manipulation affordance for 3d articulated objects via few-shot interactions
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Ar2-d2: Training a robot without a robot
Jiafei Duan, Yi Ru Wang, Mohit Shridhar, Dieter Fox, and Ranjay Krishna · 2023
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Ditto in the house: Building articulation models of indoor scenes through interactive perception
Cheng-Chun Hsu, Zhenyu Jiang, and Yuke Zhu · 2023
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What went wrong? closing the sim-to-real gap via differentiable causal discovery
Peide Huang, Xilun Zhang, Ziang Cao, Shiqi Liu, Mengdi Xu, Wenhao Ding, Jonathan Francis, Bingqing Chen, and Ding Zhao · 2023
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Sim2real 2 : Actively building explicit physics model for precise articulated object manipulation
Liqian Ma, Jiaojiao Meng, Shuntao Liu, Weihang Chen, Jing Xu, and Rui Chen · 2023
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Learning physically grounded robot vision with active sensing motor policies
Gabriel B Margolis, Xiang Fu, Yandong Ji, and Pulkit Agrawal · 2023
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Cal-QL: Calibrated offline RL pre-training for efficient online fine-tuning
Mitsuhiko Nakamoto, Yuexiang Zhai, Anikait Singh, Yi Ma, Chelsea Finn, Aviral Kumar, and Sergey Levine · 2023
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In-hand object rotation via rapid motor adaptation
Haozhi Qi, Ashish Kumar, Roberto Calandra, Yi Ma, and Jitendra Malik · 2023
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Adaptsim: Task-driven simulation adaptation for sim-to-real transfer
Allen Z Ren, Hongkai Dai, Benjamin Burchfiel, and Anirudha Majumdar · 2023
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Industreal: Transferring contact-rich assembly tasks from simulation to reality
Bingjie Tang, Michael A Lin, Iretiayo Akinola, Ankur Handa, Gaurav S Sukhatme, Fabio Ramos, Dieter Fox, and Yashraj Narang · 2023
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Optimal exploration for model-based rl in nonlinear systems
Andrew Wagenmaker, Guanya Shi, and Kevin Jamieson · 2023
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Repo: Resilient model-based reinforcement learning by regularizing posterior predictability
Chuning Zhu, Max Simchowitz, Siri Gadipudi, and Abhishek Gupta · 2023
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Offline multi-task transfer rl with representational penalization
Avinandan Bose, Simon Shaolei Du, and Maryam Fazel · 2024
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Reconciling reality through simulation: A real-to-sim-to-real approach for robust manipulation
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