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The burgeoning fields of robot learning and embodied AI have triggered an increasing demand for large quantities of data.
System identification
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Duy Nguyen-Tuong and Jan Peters · 2011
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Physically consistent state estimation and system identification for contacts
Svetoslav Kolev and Emanuel Todorov · 2015
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Transfer from simulation to real world through learning deep inverse dynamics model
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Third person imitation learning
Bradly C Stadie, Pieter Abbeel, and Ilya Sutskever · 2016
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Simulation-based design of dynamic controllers for humanoid balancing
Jie Tan, Zhaoming Xie, Byron Boots, and C Karen Liu · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Learning invariant feature spaces to transfer skills with reinforcement learning
Abhishek Gupta, Coline Devin, YuXuan Liu, Pieter Abbeel, and Sergey Levine · 2017
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Grounded action transformation for robot learning in simulation
Josiah Hanna and Peter Stone · 2017
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Virtual to real reinforcement learning for autonomous driving
Xinlei Pan, Yurong You, Ziyan Wang, and Cewu Lu · 2017
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EPOpt: Learning robust neural network policies using model ensembles
Aravind Rajeswaran, Sarvjeet Ghotra, Balaraman Ravindran, and Sergey Levine · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
Josh Tobin, Rachel Fong, Alex Ray, et al · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, et al · 2017
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Preparing for the unknown: Learning a universal policy with online system identification
Wenhao Yu, Jie Tan, C Karen Liu, and Greg Turk · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Imitation from observation: Learning to imitate behaviors from raw video via context translation
YuXuan Liu, Abhishek Gupta, Pieter Abbeel, and Sergey Levine · 2018
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Driving policy transfer via modularity and abstraction
Matthias Mueller, Alexey Dosovitskiy, Bernard Ghanem, and Vladlen Koltun · 2018
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Learning to adapt in dynamic, real-world environments through meta-reinforcement learning
Anusha Nagabandi, Ignasi Clavera, Simin Liu, et al · 2018
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Zero-shot visual imitation
Deepak Pathak, Parsa Mahmoudieh, Michael Luo, et al · 2018
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Sim-to-real transfer of robotic control with dynamics randomization
Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2018
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Sim2real viewpoint invariant visual servoing by recurrent control
Fereshteh Sadeghi, Alexander Toshev, Eric Jang, and Sergey Levine · 2018
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Time-contrastive networks: Self-supervised learning from video
Pierre Sermanet, Corey Lynch, Yevgen Chebotar, et al · 2018
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One-shot imitation from observing humans via domain-adaptive meta-learning
Tianhe Yu, Chelsea Finn, Sudeep Dasari, et al · 2018
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Learning to drive from simulation without real world labels
Alex Bewley, Jessica Rigley, Yuxuan Liu, et al · 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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Temporal cycle-consistency learning
Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman · 2019
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Meta-sim: Learning to generate synthetic datasets
Amlan Kar, Aayush Prakash, Ming-Yu Liu, Eric Cameracci, Justin Yuan, Matt Rusiniak, David Acuna, Antonio Torralba, and Sanja Fidler · 2019
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Efficient off-policy meta-reinforcement learning via probabilistic context variables
Kate Rakelly, Aurick Zhou, Chelsea Finn, Sergey Levine, and Deirdre Quillen · 2019
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Bayessim: adaptive domain randomization via probabilistic inference for robotics simulators
Fabio Ramos, Rafael Possas, and Dieter Fox · 2019
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Third-person visual imitation learning via decoupled hierarchical controller
Pratyusha Sharma, Deepak Pathak, and Abhinav Gupta · 2019
Cited alongside, same era.
How to pick the domain randomization parameters for sim-to-real transfer of reinforcement learning policies?
Quan Vuong, Sharad Vikram, Hao Su, Sicun Gao, and Henrik I Christensen · 2019
Cited alongside, same era.
Domain randomization and pyramid consistency: Simulation-to-real generalization without accessing target domain data
Xiangyu Yue, Yang Zhang, Sicheng Zhao, Alberto Sangiovanni-Vincentelli, Kurt Keutzer, and Boqing Gong · 2019
Cited alongside, same era.
Vr-goggles for robots: Real-to-sim domain adaptation for visual control
Jingwei Zhang, Lei Tai, Peng Yun, et al · 2019
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Varibad: A very good method for bayes-adaptive deep rl via meta-learning
Luisa Zintgraf, Kyriacos Shiarlis, Maximilian Igl, et al · 2019
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A survey of embodied ai: From simulators to research tasks
Jiafei Duan, Samson Yu, Hui Li Tan, Hongyuan Zhu, and Cheston Tan · 2022
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Learn what matters: cross-domain imitation learning with task-relevant embeddings
Tim Franzmeyer, Philip Torr, and João F Henriques · 2022
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Beyond pick-and-place: Tackling robotic stacking of diverse shapes
Alex X Lee, Coline Manon Devin, Yuxiang Zhou, et al · 2022
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Dara: Dynamics-aware reward augmentation in offline reinforcement learning
Jinxin Liu, Zhang Hongyin, and Donglin Wang · 2022
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Vip: Towards universal visual reward and representation via value-implicit pre-training
Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman, Osbert Bastani, Vikash Kumar, and Amy Zhang · 2022
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When to trust your simulator: Dynamics-aware hybrid offline-and-online reinforcement learning
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Learning dexterous in-hand manipulation
Marcin Andrychowicz, Bowen Baker, Maciek Chociej, et al · 2020
Cited alongside, same era.
An imitation from observation approach to transfer learning with dynamics mismatch
Siddharth Desai, Ishan Durugkar, Haresh Karnan, et al · 2020
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Off-dynamics reinforcement learning: Training for transfer with domain classifiers
Benjamin Eysenbach, Shreyas Chaudhari, Swapnil Asawa, Sergey Levine, and Ruslan Salakhutdinov · 2020
Cited alongside, same era.
State-only imitation with transition dynamics mismatch
Tanmay Gangwani and Jian Peng · 2020
Cited alongside, same era.
Task-agnostic morphology evolution
Joey Hejna, Pieter Abbeel, and Lerrel Pinto · 2020
Cited alongside, same era.
Hierarchically decoupled imitation for morphological transfer
Joey Hejna, Lerrel Pinto, and Pieter Abbeel · 2020
Cited alongside, same era.
Offline imitation learning with a misspecified simulator
Shengyi Jiang, Jingcheng Pang, and Yang Yu · 2020
Cited alongside, same era.
Haoyi Niu, Shubham Sharma, Yiwen Qiu, Ming Li, Guyue Zhou, Jianming HU, and Xianyuan Zhan · 2022
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Cross-domain transfer via semantic skill imitation
Karl Pertsch, Ruta Desai, Vikash Kumar, et al · 2022
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A generalist agent
Scott Reed, Konrad Zolna, Emilio Parisotto, et al · 2022
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A versatile and efficient reinforcement learning approach for autonomous driving
Guan Wang, Haoyi Niu, Desheng Zhu, Jianming Hu, Xianyuan Zhan, and Guyue Zhou · 2022
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Weakly supervised correspondence learning
Zihan Wang, Zhangjie Cao, Yilun Hao, and Dorsa Sadigh · 2022
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End-to-end autonomous driving: Challenges and frontiers
Li Chen, Penghao Wu, Kashyap Chitta, Bernhard Jaeger, Andreas Geiger, and Hongyang Li · 2023
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Efficient policy adaptation with contrastive prompt ensemble for embodied agents
Wonje Choi, Woo Kyung Kim, SeungHyun Kim, and Honguk Woo · 2023
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Marginalized importance sampling for off-environment policy evaluation
Pulkit Katdare, Nan Jiang, and Katherine Rose Driggs-Campbell · 2023
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Proto: Iterative policy regularized offline-to-online reinforcement learning
Jianxiong Li, Xiao Hu, Haoran Xu, Jingjing Liu, Xianyuan Zhan, and Ya-Qin Zhang · 2023
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Liv: Language-image representations and rewards for robotic control
Yecheng Jason Ma, Vikash Kumar, Amy Zhang, Osbert Bastani, and Dinesh Jayaraman · 2023
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H2o+: An improved framework for hybrid offline-and-online rl with dynamics gaps
Haoyi Niu, Tianying Ji, Bingqi Liu, Haocheng Zhao, Xiangyu Zhu, Jianying Zheng, Pengfei Huang, Guyue Zhou, Jianming Hu, and Xianyuan Zhan · 2023
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Octo: An open-source generalist robot policy, 2023
Octo Model Team, Dibya Ghosh, Homer Walke, Karl Pertsch, et al · 2023
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Open x-embodiment: Robotic learning datasets and RT-x models
Open X-Embodiment et al · 2023
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Learning robot manipulation from cross-morphology demonstration
Gautam Salhotra, I-Chun Arthur Liu, and Gaurav S. Sukhatme · 2023
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Grow your limits: Continuous improvement with real-world rl for robotic locomotion
Laura Smith, Yunhao Cao, and Sergey Levine · 2023
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Bridgedata v2: A dataset for robot learning at scale
Homer Rich Walke, Kevin Black, Tony Z Zhao, et al · 2023
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Cold diffusion on the replay buffer: Learning to plan from known good states
Zidan Wang, Takeru Oba, Takuma Yoneda, Rui Shen, Matthew Walter, and Bradly C Stadie · 2023
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Cross-domain policy adaptation via value-guided data filtering
Kang Xu, Chenjia Bai, Xiaoteng Ma, Dong Wang, Bin Zhao, Zhen Wang, Xuelong Li, and Wei Li · 2023
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Xskill: Cross embodiment skill discovery
Mengda Xu, Zhenjia Xu, Cheng Chi, Manuela Veloso, and Shuran Song · 2023
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State regularized policy optimization on data with dynamics shift
Zhenghai Xue, Qingpeng Cai, Shuchang Liu, Dong Zheng, Peng Jiang, Kun Gai, and Bo An · 2023
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Polybot: Training one policy across robots while embracing variability
Jonathan Heewon Yang, Dorsa Sadigh, and Chelsea Finn · 2023
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Multi-embodiment legged robot control as a sequence modeling problem
Chen Yu, Weinan Zhang, Hang Lai, Zheng Tian, Laurent Kneip, and Jun Wang · 2023
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Decisionnce: Embodied multimodal representations via implicit preference learning
Jianxiong Li, Jinliang Zheng, Yinan Zheng, Liyuan Mao, Xiao Hu, Sijie Cheng, Haoyi Niu, et al · 2024
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