Fetching the paper…
Reading the bibliography…
Robotic skill learning has been increasingly studied but the demonstration collections are more challenging compared to collecting images/videos in computer vision and texts in natural language processing.
D. A. Pomerleau, Alvinn: An autonomous land vehicle in a neural network, In Advances in neural information processing systems, 305–313, 1989
1989
Earlier work this paper cites.
K. Muelling, A. Boularias, B. Mohler, B. Scholkopf, and J. Peters, Learning strategies in table tennis using inverse reinforcement learning, Biological cybernetics, 108(5):603–619, 2014
2014
Earlier work this paper cites.
Peter Kazanzides, Zihan Chen, Anton Deguet, Gregory S. Fischer, Russell H. Taylor, and Simon P. DiMaio, An open-source research kit for the da Vinci® Surgical System, 2014 IEEE International Conference on Robotics and Automation (ICRA), 6434-6439, 2014
2014
Earlier work this paper cites.
David Kent, Carl Saldanha,and Sonia Chernova, A Comparison of Remote Robot Teleoperation Interfaces for General Object Manipulation, 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2017
2017
Earlier work this paper cites.
Jacky Liang, Jeffrey Mahler, Michael Laskey, Pusong Li, and Ken Goldberg, Using dVRK Teleoperation to Facilitate Deep Learning of Automation Tasks for an Industrial Robot, 13th IEEE Conference on Automation Science and Engineering (CASE), 1-8, 2017
2017
Earlier work this paper cites.
Ajay Mandlekar, Yuke Zhu, Animesh Garg, Jonathan Booher, Max Spero, Albert Tung, Julian Gao, John Emmons, Anchit Gupta, Emre Orbay, Silvio Savarese, and Li Fei-Fei, Roboturk: A crowdsourcing platform for robotic skill learning through imitation, Conference on Robot Learning, 879-893, 2018
2018
Earlier work this paper cites.
Jeffrey I. Lipton, Aidan J. Fay, and Daniela Rus, Baxter’s homunculus: Virtual reality spaces for teleoperation in manufacturing, IEEE Robotics and Automation Letters, 3(1):179–186, 2018
2018
Earlier work this paper cites.
P. Sharma, L. Mohan, L. Pinto, and A. Gupta, Multiple interactions made easy (mime): Large scale demonstrations data for imitation, CoRL, 2018
2018
Earlier work this paper cites.
Yuke Zhu, Ziyu Wang, Josh Merel, Andrei Rusu, Tom Erez, Serkan Cabi, Saran Tunyasuvunakool, Janos Kramar, Raia Hadsell, Nando de Freitas, and Nicolas Heess, Reinforcement and Imitation Learning for Diverse Visuomotor Skills, Proceedings of Robotics: Science and Systems, 2018
2018
Cited alongside, same era.
S. Fujimoto, D. Meger, and D. Precup, Off-policy deep reinforcement learning without exploration, In International Conference on Machine Learning, 2052–2062, 2019
2019
Cited alongside, same era.
Hongbin Lin, Chiu-Wai Vincent Hui, Yan Wang, Anton Deguet, Peter Kazanzides, and K. W. Samuel Au, A Reliable Gravity Compensation Control Strategy for dVRK Robotic Arms With Nonlinear Disturbance Forces, IEEE Robotics and Automation Letters, 4(4): 3892-3899, 2019
2019
Cited alongside, same era.
Albert Tung, Josiah Wong, Ajay Mandlekar, Roberto Martín-Martín, Yuke Zhu, Li Fei-Fei, and Silvio Savarese, Learning Multi-Arm Manipulation Through Collaborative Teleoperation, IEEE International Conference on Robotics and Automation (ICRA), 9212-9219, 2021
2021
Later among the works it cites.
Ajay Mandlekar, Danfei Xu, Josiah Wong, Soroush Nasiriany, Chen Wang, Rohun Kulkarni, Li Fei-Fei, Silvio Savarese, Yuke Zhu, and Roberto Martín-Martín, What Matters in Learning from Offline Human Demonstrations for Robot Manipulation, Conference on Robot Learning (CoRL), 2021
2021
Later among the works it cites.
Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, and Abhinav Gupta, R3M: A Universal Visual Representation for Robot Manipulation, Conference on Robot Learning (CoRL) 2022
2022
Later among the works it cites.
Chengshu Li, Fei Xia, Roberto Martín-Martín, Michael Lingelbach, Sanjana Srivastava, Bokui Shen, Kent Vainio, Cem Gokmen, Gokul Dharan, Tanish Jain, Andrey Kurenkov, C. Karen Liu, Hyowon Gweon, Jiajun Wu, Li Fei-Fei, and Silvio Savarese, iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks, Proceedings of the 5th Conference on Robot Learning, 455-465, 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
S. Song, A. Zeng, J. Lee, and T. Funkhouser, Grasping in the wild: Learning 6dof closed-loop grasping from low-cost demonstrations, IEEE Robotics and Automation Letters (RA-L), 5(3): 4978-4985, 2020
2020
Cited alongside, same era.
Sarah Young, Dhiraj Gandhi, Shubham Tulsiani, Abhinav Gupta, Pieter Abbeel, and Lerrel Pinto, Visual Imitation Made Easy, 4th Conference on Robot Learning (CoRL), 2020
2020
Cited alongside, same era.
A. Mandlekar, D. Xu, R. Martín-Martín, S. Savarese, and L. Fei-Fei, GTI: Learning to Generalize across Long-Horizon Tasks from Human Demonstrations, Robotics: Science and Systems, 2020
2020
Cited alongside, same era.
A. Kumar, A. Zhou, G. Tucker, and S. Levine, Conservative Q-learning for offline reinforcement learning, Conference on Neural Information Processing Systems (NeurIPS), 2020
2020
Cited alongside, same era.
Cited in the paper.
2022
Later among the works it cites.
Abhishek Padalkar et al., Open X-Embodiment: Robotic Learning Datasets and RT-X Models, https://robotics-transformer-x.github.io/paper.pdf, 2023
2023
Closest in time.
Sridhar Pandian Arunachalam, Irmak Güzey, Soumith Chintala, and Lerrel Pinto, Holo-Dex: Teaching Dexterity with Immersive Mixed Reality, 2023 IEEE International Conference on Robotics and Automation (ICRA 2023), pp. 5962-5969, 2023
2023
Closest in time.
Tony Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn, Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware, Robotics: Science and Systems, 2023
2023
Closest in time.
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, ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills, International Conference on Learning Representations, 2023
2023
Closest in time.