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Imitation learning with human data has demonstrated remarkable success in teaching robots in a wide range of skills.
Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Learning structured output representation using deep conditional generative models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan · 2015
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Infogail: Interpretable imitation learning from visual demonstrations
Yunzhu Li, Jiaming Song, and Stefano Ermon · 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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Roboturk: A crowdsourcing platform for robotic skill learning through imitation
Ajay Mandlekar, Yuke Zhu, Animesh Garg, Jonathan Booher, Max Spero, Albert Tung, Julian Gao, John Emmons, Anchit Gupta, Emre Orbay, et al · 2018
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Neural probabilistic motor primitives for humanoid control
Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, and Nicolas Heess · 2018
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An algorithmic perspective on imitation learning
Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J Andrew Bagnell, Pieter Abbeel, Jan Peters, et al · 2018
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Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning
Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, and Karol Hausman · 2019
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D4rl: Datasets for deep data-driven reinforcement learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
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Rl unplugged: Benchmarks for offline reinforcement learning
Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel Mankowitz, Cosmin Paduraru, et al · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 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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One solution is not all you need: Few-shot extrapolation via structured maxent rl
Saurabh Kumar, Aviral Kumar, Sergey Levine, and Chelsea Finn · 2020
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Learning latent plans from play
Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, and Pierre Sermanet · 2020
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Hyperparameter selection for offline reinforcement learning
Tom Le Paine, Cosmin Paduraru, Andrea Michi, Caglar Gulcehre, Konrad Zolna, Alexander Novikov, Ziyu Wang, and Nando de Freitas · 2020
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine · 2020
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Behavior transformers: Cloning k k modes with one stone
Nur Muhammad Shafiullah, Zichen Cui, Ariuntuya Arty Altanzaya, and Lerrel Pinto · 2022
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Roboagent: Generalization and efficiency in robot manipulation via semantic augmentations and action chunking, 2023
Homanga Bharadhwaj, Jay Vakil, Mohit Sharma, Abhinav Gupta, Shubham Tulsiani, and Vikash Kumar · 2023
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Diffusion policy: Visuomotor policy learning via action diffusion
Cheng Chi, Siyuan Feng, Yilun Du, Zhenjia Xu, Eric Cousineau, Benjamin Burchfiel, and Shuran Song · 2023
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Arnold: A benchmark for language-grounded task learning with continuous states in realistic 3d scenes
Ran Gong, Jiangyong Huang, Yizhou Zhao, Haoran Geng, Xiaofeng Gao, Qingyang Wu, Wensi Ai, Ziheng Zhou, Demetri Terzopoulos, Song-Chun Zhu, et al · 2023
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Maniskill2: A unified benchmark for generalizable manipulation skills
Jiayuan Gu, Fanbo Xiang, Xuanlin Li, Zhan Ling, Xiqiang Liu, Tongzhou Mu, Yihe Tang, Stone Tao, Xinyue Wei, Yunchao Yao, Xiaodi Yuan, Pengwei Xie, Zhiao Huang, Rui Chen, and Hao Su · 2023
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Benchmarks for deep off-policy evaluation
Justin Fu, Mohammad Norouzi, Ofir Nachum, George Tucker, Ziyu Wang, Alexander Novikov, Mengjiao Yang, Michael R Zhang, Yutian Chen, Aviral Kumar, et al · 2021
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What matters in learning from offline human demonstrations for robot manipulation
Ajay Mandlekar, Danfei Xu, Josiah Wong, Soroush Nasiriany, Chen Wang, Rohun Kulkarni, Li Fei-Fei, Silvio Savarese, Yuke Zhu, and Roberto Martín-Martín · 2021
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Maniskill: Generalizable manipulation skill benchmark with large-scale demonstrations
Tongzhou Mu, Zhan Ling, Fanbo Xiang, Derek Yang, Xuanlin Li, Stone Tao, Zhiao Huang, Zhiwei Jia, and Hao Su · 2021
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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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Rt-1: Robotics transformer for real-world control at scale
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Tomas Jackson, Sally Jesmonth, Nikhil Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Isabel Leal, Kuang-Huei Lee, Sergey Levine, Yao Lu, Utsav Malla, Deeksha Manjunath, Igor Mordatch, Ofir Nachum, Carolina Parada, Jodilyn Peralta, Emily Perez, Karl Pertsch, Jornell Quiambao, Kanishka Rao, Michael Ryoo, Grecia Salazar, Pannag Sanketi, Kevin Sayed, Jaspiar Singh, Sumedh Sontakke, Austin Stone, Clayton Tan, Huong Tran, Vincent Vanhoucke, Steve Vega, Quan Vuong, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Tianhe Yu, and Brianna Zitkovich · 2022
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Specializing versatile skill libraries using local mixture of experts
Onur Celik, Dongzhuoran Zhou, Ge Li, Philipp Becker, and Gerhard Neumann · 2022
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Implicit behavioral cloning
Pete Florence, Corey Lynch, Andy Zeng, Oscar A Ramirez, Ayzaan Wahid, Laura Downs, Adrian Wong, Johnny Lee, Igor Mordatch, and Jonathan Tompson · 2022
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Furniturebench: Reproducible real-world benchmark for long-horizon complex manipulation
Minho Heo, Youngwoon Lee, Doohyun Lee, and Joseph J. Lim · 2023
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Vima: General robot manipulation with multimodal prompts
Yunfan Jiang, Agrim Gupta, Zichen Zhang, Guanzhi Wang, Yongqiang Dou, Yanjun Chen, Li Fei-Fei, Anima Anandkumar, Yuke Zhu, and Linxi Fan · 2023
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Libero: Benchmarking knowledge transfer for lifelong robot learning, 2023
Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, and Peter Stone · 2023
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Interactive language: Talking to robots in real time
Corey Lynch, Ayzaan Wahid, Jonathan Tompson, Tianli Ding, James Betker, Robert Baruch, Travis Armstrong, and Pete Florence · 2023
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Imitating human behaviour with diffusion models
Tim Pearce, Tabish Rashid, Anssi Kanervisto, Dave Bignell, Mingfei Sun, Raluca Georgescu, Sergio Valcarcel Macua, Shan Zheng Tan, Ida Momennejad, Katja Hofmann, et al · 2023
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Goal-conditioned imitation learning using score-based diffusion policies
Moritz Reuss, Maximilian Li, Xiaogang Jia, and Rudolf Lioutikov · 2023
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Bridgedata v2: A dataset for robot learning at scale
Homer Rich Walke, Kevin Black, Tony Z. Zhao, Quan Vuong, Chongyi Zheng, Philippe Hansen-Estruch, Andre Wang He, Vivek Myers, Moo Jin Kim, Max Du, Abraham Lee, Kuan Fang, Chelsea Finn, and Sergey Levine · 2023
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Learning fine-grained bimanual manipulation with low-cost hardware
Tony Z Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn · 2023
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