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Learning from demonstration is a powerful method for teaching robots new skills, and having more demonstration data often improves policy learning.
Trajectory-tracking and path-following of underactuated autonomous vehicles with parametric modeling uncertainty
A Pedro Aguiar and Joao P Hespanha · 2007
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Deepmimic: Example-guided deep reinforcement learning of physics-based character skills
Xue Bin Peng, Pieter Abbeel, Sergey Levine, and Michiel Van de Panne · 2018
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Time-contrastive networks: Self-supervised learning from video
Pierre Sermanet, Corey Lynch, Yevgen Chebotar, Jasmine Hsu, Eric Jang, Stefan Schaal, Sergey Levine, and Google Brain · 2018
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Time-contrastive networks: Self-supervised learning from video
Pierre Sermanet, Corey Lynch, Yevgen Chebotar, Jasmine Hsu, Eric Jang, Stefan Schaal, Sergey Levine, and Google Brain · 2018
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Behavioral cloning from observation, 2018
Faraz Torabi, Garrett Warnell, and Peter Stone · 2018
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Deep imitation learning for complex manipulation tasks from virtual reality teleoperation
Tianhao Zhang, Zoe McCarthy, Owen Jow, Dennis Lee, Xi Chen, Ken Goldberg, and Pieter Abbeel · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Learning predictive models from observation and interaction, 2019
Karl Schmeckpeper, Annie Xie, Oleh Rybkin, Stephen Tian, Kostas Daniilidis, Sergey Levine, and Chelsea Finn · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Keypoints into the future: Self-supervised correspondence in model-based reinforcement learning, 2020
Lucas Manuelli, Yunzhu Li, Pete Florence, and Russ Tedrake · 2020
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Keto: Learning keypoint representations for tool manipulation
Zengyi Qin, Kuan Fang, Yuke Zhu, Li Fei-Fei, and Silvio Savarese · 2020
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Vilt: Vision-and-language transformer without convolution or region supervision, 2021
Wonjae Kim, Bokyung Son, and Ildoo Kim · 2021
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3d neural scene representations for visuomotor control
Yunzhu Li, Shuang Li, Vincent Sitzmann, Pulkit Agrawal, and Antonio Torralba · 2021
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Learning visible connectivity dynamics for cloth smoothing
Xingyu Lin, Yufei Wang, Zixuan Huang, and David Held · 2021
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Concept2robot: Learning manipulation concepts from instructions and human demonstrations
Lin Shao, Toki Migimatsu, Qiang Zhang, Karen Yang, and Jeannette Bohg · 2021
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Learning by watching: Physical imitation of manipulation skills from human videos
Haoyu Xiong, Quanzhou Li, Yun-Chun Chen, Homanga Bharadhwaj, Samarth Sinha, and Animesh Garg · 2021
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Video pretraining (vpt): Learning to act by watching unlabeled online videos
Bowen Baker, Ilge Akkaya, Peter Zhokov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro, and Jeff Clune · 2022
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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, et al · 2022
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Ifor: Iterative flow minimization for robotic object rearrangement
Ankit Goyal, Arsalan Mousavian, Chris Paxton, Yu-Wei Chao, Brian Okorn, Jia Deng, and Dieter Fox · 2022
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Particle video revisited: Tracking through occlusions using point trajectories
Adam W Harley, Zhaoyuan Fang, and Katerina Fragkiadaki · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Mesh-based dynamics model with occlusion reasoning for cloth manipulation
Zixuan Huang, Xingyu Lin, and David Held · 2022
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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, Piotr Dollar, and Ross Girshick · 2023
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Learning to Act from Actionless Video through Dense Correspondences
Po-Chen Ko, Jiayuan Mao, Yilun Du, Shao-Hua Sun, and Joshua B Tenenbaum · 2023
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Spawnnet: Learning generalizable visuomotor skills from pre-trained networks
Xingyu Lin, John So, Sashwat Mahalingam, Fangchen Liu, and Pieter Abbeel · 2023
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Libero: Benchmarking knowledge transfer for lifelong robot learning
Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Yuke Zhu, Peter Stone, et al · 2023
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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 · 2023
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Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, et al · 2022
Cited alongside, same era.
Reinforcement learning with action-free pre-training from videos, 2022
Younggyo Seo, Kimin Lee, Stephen James, and Pieter Abbeel · 2022
Cited alongside, same era.
Fighting fire with fire: Avoiding dnn shortcuts through priming
Chuan Wen, Jianing Qian, Jierui Lin, Jiaye Teng, Dinesh Jayaraman, and Yang Gao · 2022
Cited alongside, same era.
Affordances from human videos 417 as a versatile representation for robotics
S Bahl, R Mendonca, L Chen, U Jain, and D Pathak · 2023
Cited alongside, same era.
Zero-shot robot manipulation from passive human videos
Homanga Bharadhwaj, Abhinav Gupta, Shubham Tulsiani, and Vikash Kumar · 2023
Cited alongside, same era.
Zero-shot robotic manipulation with pretrained image-editing diffusion models
Kevin Black, Mitsuhiko Nakamoto, Pranav Atreya, Homer Walke, Chelsea Finn, Aviral Kumar, and Sergey Levine · 2023
Cited alongside, same era.
Diffusion Policy: Visuomotor Policy Learning via Action Diffusion
Cheng Chi, Siyuan Feng, Yilun Du, Zhenjia Xu, Eric Cousineau, Benjamin CM Burchfiel, and Shuran Song · 2023
Cited alongside, same era.
Structured world models from human videos
Russell Mendonca, Shikhar Bahl, and Deepak Pathak · 2023
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R3m: A universal visual representation for robot manipulation
Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, and Abhinav Gupta · 2023
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Open x-embodiment: Robotic learning datasets and rt-x models
Abhishek Padalkar, Acorn Pooley, Ajinkya Jain, Alex Bewley, Alex Herzog, Alex Irpan, Alexander Khazatsky, Anant Rai, Anikait Singh, Anthony Brohan, et al · 2023
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Language embedded radiance fields for zero-shot task-oriented grasping
Adam Rashid, Satvik Sharma, Chung Min Kim, Justin Kerr, Lawrence Yunliang Chen, Angjoo Kanazawa, and Ken Goldberg · 2023
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Toolflownet: Robotic manipulation with tools via predicting tool flow from point clouds
Daniel Seita, Yufei Wang, Sarthak J Shetty, Edward Yao Li, Zackory Erickson, and David Held · 2023
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Videodex: Learning dexterity from internet videos
Kenneth Shaw, Shikhar Bahl, and Deepak Pathak · 2023
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Robotap: Tracking arbitrary points for few-shot visual imitation
Mel Vecerik, Carl Doersch, Yi Yang, Todor Davchev, Yusuf Aytar, Guangyao Zhou, Raia Hadsell, Lourdes Agapito, and Jon Scholz · 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, et al · 2023
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Gello: A general, low-cost, and intuitive teleoperation framework for robot manipulators
Philipp Wu, Yide Shentu, Zhongke Yi, Xingyu Lin, and Pieter Abbeel · 2023
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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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Pointodyssey: A large-scale synthetic dataset for long-term point tracking
Yang Zheng, Adam W Harley, Bokui Shen, Gordon Wetzstein, and Leonidas J Guibas · 2023
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Learning to act without actions
Dominik Schmidt and Minqi Jiang · 2024
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