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We present Universal Manipulation Interface (UMI) -- a data collection and policy learning framework that allows direct skill transfer from in-the-wild human demonstrations to deployable robot policies.
Towards a personal robotics development platform: Rationale and design of an intrinsically safe personal robot
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Learning of compliant human–robot interaction using full-body haptic interface
Luka Peternel and Jan Babič · 2013
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Humanoid robot teleoperation with vibrotactile based balancing feedback
Anais Brygo, Ioannis Sarakoglou, Nadia Garcia-Hernandez, and Nikolaos Tsagarakis · 2014
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Automatic generation and detection of highly reliable fiducial markers under occlusion
S. Garrido-Jurado, R. Muñoz-Salinas, F.J. Madrid-Cuevas, and M.J. Marín-Jiménez · 2014
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The ycb object and model set: Towards common benchmarks for manipulation research
Berk Calli, Arjun Singh, Aaron Walsman, Siddhartha Srinivasa, Pieter Abbeel, and Aaron M. Dollar · 2015
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Deep residual learning for image recognition. corr abs/1512.03385 (2015), 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Benefit of large field-of-view cameras for visual odometry
Zichao Zhang, Henri Rebecq, Christian Forster, and Davide Scaramuzza · 2016
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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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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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Characterizing input methods for human-to-robot demonstrations
Pragathi Praveena, Guru Subramani, Bilge Mutlu, and Michael Gleicher · 2019
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Recent advances in robot learning from demonstration
Harish Ravichandar, Athanasios S Polydoros, Sonia Chernova, and Aude Billard · 2020
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Learning predictive models from observation and interaction
Karl Schmeckpeper, Annie Xie, Oleh Rybkin, Stephen Tian, Kostas Daniilidis, Sergey Levine, and Chelsea Finn · 2020
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Grasping in the wild: Learning 6dof closed-loop grasping from low-cost demonstrations
Shuran Song, Andy Zeng, Johnny Lee, and Thomas Funkhouser · 2020
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Orb-slam3: An accurate open-source library for visual, visual–inertial, and multimap slam
Carlos Campos, Richard Elvira, Juan J. Gómez Rodríguez, José M. M. Montiel, and Juan D. Tardós · 2021
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Learning generalizable robotic reward functions from “in-the-wild” human videos
Annie S Chen, Suraj Nair, and Chelsea Finn · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Reinforcement learning with videos: Combining offline observations with interaction
Karl Schmeckpeper, Oleh Rybkin, Kostas Daniilidis, Sergey Levine, and Chelsea Finn · 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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Visual imitation made easy
Sarah Young, Dhiraj Gandhi, Shubham Tulsiani, Abhinav Gupta, Pieter Abbeel, and Lerrel Pinto · 2021
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Human-to-robot imitation in the wild
Shikhar Bahl, Abhinav Gupta, and Deepak Pathak · 2022
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Bridge data: Boosting generalization of robotic skills with cross-domain datasets
Frederik Ebert, Yanlai Yang, Karl Schmeckpeper, Bernadette Bucher, Georgios Georgakis, Kostas Daniilidis, Chelsea Finn, and Sergey Levine · 2022
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Bc-z: Zero-shot task generalization with robotic imitation learning
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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Low-cost exoskeletons for learning whole-arm manipulation in the wild
Hongjie Fang, Hao-Shu Fang, Yiming Wang, Jieji Ren, Jingjing Chen, Ruo Zhang, Weiming Wang, and Cewu Lu · 2023
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Gpmf introuction: Parser for gpmf™ formatted telemetry data used within gopro® cameras
GoPro Inc · 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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Tax-pose: Task-specific cross-pose estimation for robot manipulation
Chuer Pan, Brian Okorn, Harry Zhang, Ben Eisner, and David Held · 2023
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Eric Jang, Alex Irpan, Mohi Khansari, Daniel Kappler, Frederik Ebert, Corey Lynch, Sergey Levine, and Chelsea Finn · 2022
Cited alongside, same era.
Giving robots a hand: Broadening generalization via hand-centric human video demonstrations
Moo Jin Kim, Jiajun Wu, and Chelsea Finn · 2022
Cited alongside, same era.
R3m: A universal visual representation for robot manipulation
Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, and Abhinav Gupta · 2022
Cited alongside, same era.
The surprising effectiveness of representation learning for visual imitation
Jyothish Pari, Nur Muhammad Shafiullah, Sridhar Pandian Arunachalam, and Lerrel Pinto · 2022
Cited alongside, same era.
Dexmv: Imitation learning for dexterous manipulation from human videos
Yuzhe Qin, Yueh-Hua Wu, Shaowei Liu, Hanwen Jiang, Ruihan Yang, Yang Fu, and Xiaolong Wang · 2022
Cited alongside, same era.
Neural descriptor fields: Se (3)-equivariant object representations for manipulation
Anthony Simeonov, Yilun Du, Andrea Tagliasacchi, Joshua B Tenenbaum, Alberto Rodriguez, Pulkit Agrawal, and Vincent Sitzmann · 2022
Cited alongside, same era.
SEED: Series elastic end effectors in 6d for visuotactile tool use
H.J. Terry Suh, Naveen Kuppuswamy, Tao Pang, Paul Mitiguy, Alex Alspach, and Russ Tedrake · 2022
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Latent plans for task-agnostic offline reinforcement learning
Erick Rosete-Beas, Oier Mees, Gabriel Kalweit, Joschka Boedecker, and Wolfram Burgard · 2023
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Scalable. intuitive human to robot skill transfer with wearable human machine interfaces: On complex, dexterous tasks
Felipe Sanches, Geng Gao, Nathan Elangovan, Ricardo V Godoy, Jayden Chapman, Ke Wang, Patrick Jarvis, and Minas Liarokapis · 2023
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Deep imitation learning for humanoid loco-manipulation through human teleoperation
Mingyo Seo, Steve Han, Kyutae Sim, Seung Hyeon Bang, Carlos Gonzalez, Luis Sentis, and Yuke Zhu · 2023
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Nur Muhammad Mahi Shafiullah, Anant Rai, Haritheja Etukuru, Yiqian Liu, Ishan Misra, Soumith Chintala, and Lerrel Pinto · 2023
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Videodex: Learning dexterity from internet videos
Kenneth Shaw, Shikhar Bahl, and Deepak Pathak · 2023
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Distilled feature fields enable few-shot language-guided manipulation
William Shen, Ge Yang, Alan Yu, Jansen Wong, Leslie Pack Kaelbling, and Phillip Isola · 2023
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A force-sensitive exoskeleton for teleoperation: An application in elderly care robotics
Alexander Toedtheide, Xiao Chen, Hamid Sadeghian, Abdeldjallil Naceri, and Sami Haddadin · 2023
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Mimicplay: Long-horizon imitation learning by watching human play
Chen Wang, Linxi Fan, Jiankai Sun, Ruohan Zhang, Li Fei-Fei, Danfei Xu, Yuke Zhu, and Anima Anandkumar · 2023
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GELLO: A general, low-cost, and intuitive teleoperation framework for robot manipulators
Philipp Wu, Fred Shentu, Xingyu Lin, and Pieter Abbeel · 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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Viola: Imitation learning for vision-based manipulation with object proposal priors
Yifeng Zhu, Abhishek Joshi, Peter Stone, and Yuke Zhu · 2023
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Mobile aloha: Learning bimanual mobile manipulation with low-cost whole-body teleoperation
Zipeng Fu, Tony Z Zhao, and Chelsea Finn · 2024
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