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Transferring policies learned in simulation to the real world is a promising strategy for acquiring robot skills at scale.
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Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
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Wenhao Yu, Jie Tan, C Karen Liu, and Greg Turk · 2017
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Lerrel Pinto, James Davidson, Rahul Sukthankar, and Abhinav Gupta · 2017
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Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2018
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Sim-to-real: Learning agile locomotion for quadruped robots
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Using simulation and domain adaptation to improve efficiency of deep robotic grasping
Konstantinos Bousmalis, Alex Irpan, Paul Wohlhart, Yunfei Bai, Matthew Kelcey, Mrinal Kalakrishnan, Laura Downs, Julian Ibarz, Peter Pastor, Kurt Konolige, et al · 2018
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Solving rubik’s cube with a robot hand
Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, et al · 2019
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Bayessim: adaptive domain randomization via probabilistic inference for robotics simulators
Fabio Ramos, Rafael Carvalhaes Possas, and Dieter Fox · 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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Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to-canonical adaptation networks
Stephen James, Paul Wohlhart, Mrinal Kalakrishnan, Dmitry Kalashnikov, Alex Irpan, Julian Ibarz, Sergey Levine, Raia Hadsell, and Konstantinos Bousmalis · 2019
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Learning dexterous in-hand manipulation
OpenAI: Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Jozefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, et al · 2020
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Learning quadrupedal locomotion over challenging terrain
Joonho Lee, Jemin Hwangbo, Lorenz Wellhausen, Vladlen Koltun, and Marco Hutter · 2020
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Active domain randomization
Bhairav Mehta, Manfred Diaz, Florian Golemo, Christopher J Pal, and Liam Paull · 2020
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Rma: Rapid motor adaptation for legged robots
Ashish Kumar, Zipeng Fu, Deepak Pathak, and Jitendra Malik · 2021
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Data-efficient domain randomization with bayesian optimization
Fabio Muratore, Christian Eilers, Michael Gienger, and Jan Peters · 2021
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Dynamics randomization revisited: A case study for quadrupedal locomotion
Mathematical discoveries from program search with large language models
Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Matej Balog, M Pawan Kumar, Emilien Dupont, Francisco JR Ruiz, Jordan S Ellenberg, Pengming Wang, Omar Fawzi, et al · 2023
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Walk these ways: Tuning robot control for generalization with multiplicity of behavior
Gabriel B Margolis and Pulkit Agrawal · 2023
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Dextreme: Transfer of agile in-hand manipulation from simulation to reality
Ankur Handa, Arthur Allshire, Viktor Makoviychuk, Aleksei Petrenko, Ritvik Singh, Jingzhou Liu, Denys Makoviichuk, Karl Van Wyk, Alexander Zhurkevich, Balakumar Sundaralingam, et al · 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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Leap hand: Low-cost, efficient, and anthropomorphic hand for robot learning
Kenneth Shaw, Ananye Agarwal, and Deepak Pathak · 2023
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Zhaoming Xie, Xingye Da, Michiel Van de Panne, Buck Babich, and Animesh Garg · 2021
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Isaac gym: High performance gpu-based physics simulation for robot learning
Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, et al · 2021
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Do as i can, not as i say: Grounding language in robotic affordances
Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Chuyuan Fu, Keerthana Gopalakrishnan, Karol Hausman, et al · 2022
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Robot learning from randomized simulations: A review
Fabio Muratore, Fabio Ramos, Greg Turk, Wenhao Yu, Michael Gienger, and Jan Peters · 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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Rapid locomotion via reinforcement learning
Gabriel B Margolis, Ge Yang, Kartik Paigwar, Tao Chen, and Pulkit Agrawal · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Large language models as generalizable policies for embodied tasks
Andrew Szot, Max Schwarzer, Harsh Agrawal, Bogdan Mazoure, Walter Talbott, Katherine Metcalf, Natalie Mackraz, Devon Hjelm, and Alexander Toshev · 2023
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Saytap: Language to quadrupedal locomotion
Yujin Tang, Wenhao Yu, Jie Tan, Heiga Zen, Aleksandra Faust, and Tatsuya Harada · 2023
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Llm+ p: Empowering large language models with optimal planning proficiency
Bo Liu, Yuqian Jiang, Xiaohan Zhang, Qiang Liu, Shiqi Zhang, Joydeep Biswas, and Peter Stone · 2023
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Generalized planning in pddl domains with pretrained large language models
Tom Silver, Soham Dan, Kavitha Srinivas, Joshua B Tenenbaum, Leslie Pack Kaelbling, and Michael Katz · 2023
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Task and motion planning with large language models for object rearrangement
Yan Ding, Xiaohan Zhang, Chris Paxton, and Shiqi Zhang · 2023
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Text2motion: From natural language instructions to feasible plans
Kevin Lin, Christopher Agia, Toki Migimatsu, Marco Pavone, and Jeannette Bohg · 2023
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Domain randomization via entropy maximization
Gabriele Tiboni, Pascal Klink, Jan Peters, Tatiana Tommasi, Carlo D’Eramo, and Georgia Chalvatzaki · 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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Scaling up and distilling down: Language-guided robot skill acquisition
Huy Ha, Pete Florence, and Shuran Song · 2023
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The perils of trial-and-error reward design: misdesign through overfitting and invalid task specifications
Serena Booth, W Bradley Knox, Julie Shah, Scott Niekum, Peter Stone, and Alessandro Allievi · 2023
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Not only rewards but also constraints: Applications on legged robot locomotion
Yunho Kim, Hyunsik Oh, Jeonghyun Lee, Jinhyeok Choi, Gwanghyeon Ji, Moonkyu Jung, Donghoon Youm, and Jemin Hwangbo · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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Dribblebot: Dynamic legged manipulation in the wild
Yandong Ji, Gabriel B Margolis, and Pulkit Agrawal · 2023
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Large language models to enhance bayesian optimization, 2024
Anonymous · 2024
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