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Reinforcement learning (RL) can in principle let robots automatically adapt to new tasks, but current RL methods require a large number of trials to accomplish this.
“Peg-on-hole: a model based solution to peg and hole alignment”
Herman Bruyninckx, Stefan Dutré and Joris Schutter · 1924
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“Force Feedback Control of Manipulator Fine Motions”
D. Whitney · 1977
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“Quasi-Static Assembly of Compliantly Supported Rigid Parts”
D.. Whitney · 1982
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“Robotic assembly: chamferless peg-hole assembly”
W. Haskiya, K. Maycock and J. Knight · 1999
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“Search strategies for peg-in-hole assemblies with position uncertainty”
S. Chhatpar and M. Branicky · 2001
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“Interpretation of force and moment signals for compliant peg-in-hole assembly”
W.S. Newman, Y. Zhao and Y.-H. Pao · 2001
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“Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems”, 2020
Sergey Levine, Aviral Kumar, George Tucker and Justin Fu · 2005
Earlier work this paper cites.
“Conservative Q-Learning for Offline Reinforcement Learning”, 2020
Aviral Kumar, Aurick Zhou, George Tucker and Sergey Levine · 2006
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“Accelerating Online Reinforcement Learning with Offline Datasets”, 2020
Ashvin Nair, Murtaza Dalal, Abhishek Gupta and Sergey Levine · 2006
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“Offline Meta Reinforcement Learning”
Ron Dorfman and Aviv Tamar · 2008
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“MELD: Meta-Reinforcement Learning from Images via Latent State Models”
Tony. Zhao, Anusha Nagabandi, Kate Rakelly, Chelsea Finn and Sergey Levine · 2010
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“COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning”, 2020
Avi Singh, Albert Yu, Jonathan Yang, Jesse Zhang, Aviral Kumar and Sergey Levine · 2010
Earlier work this paper cites.
“Intuitive peg-in-hole assembly strategy with a compliant manipulator”
Hyeonjun Park, J. Bae, Jae-Han Park, M. Baeg and Jaeheung Park · 2013
Earlier work this paper cites.
“Intelligent and environment-independent Peg-In-Hole search strategies”
Kamal Sharma, Varsha Shirwalkar and Prabir. Pal · 2013
Earlier work this paper cites.
“Learning Contact-Rich Manipulation Skills with Guided Policy Search”
Sergey Levine, Nolan Wagener and Pieter Abbeel · 2015
Earlier work this paper cites.
“Learning from the Hindsight Plan – Episodic MPC Improvement”
A. Tamar, G. Thomas, T. Zhang, S. Levine and P. Abbeel · 2016
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“End-to-end training of deep visuomotor policies”
Sergey Levine, Chelsea Finn, Trevor Darrell and Pieter Abbeel · 2016
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“RL 2 : Fast Reinforcement Learning via Slow Reinforcement Learning”, 2016
Yan Duan, John Schulman, Xi Chen, Peter. Bartlett, Ilya Sutskever and Pieter Abbeel · 2016
Cited alongside, same era.
“Continuous control with deep reinforcement learning”
T. Lillicrap, Jonathan. Hunt, A. Pritzel, N. Heess, T. Erez, Yuval Tassa, D. Silver and Daan Wierstra · 2016
Cited alongside, same era.
“Autonomous alignment of peg and hole by force/torque measurement for robotic assembly”
Te Tang, Hsien-Chung Lin, Yu Zhao, Wenjie Chen and Masayoshi Tomizuka · 2016
Cited alongside, same era.
“Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards”
Mel Vecerik, Todd Hester, Jonathan Scholz, Fumin Wang, Olivier Pietquin, Bilal Piot, Nicolas Heess, Thomas Rothörl, Thomas Lampe and Martin Riedmiller · 2017
Cited alongside, same era.
“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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“Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning”
Mitchell Wortsman, Kiana Ehsani, Mohammad Rastegari, Ali Farhadi and Roozbeh Mottaghi · 2019
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“Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards”
Gerrit Schoettler, Ashvin Nair, Jianlan Luo, Shikhar Bahl, Juan Aparicio, Eugen Solowjow and Sergey Levine · 2020
Later among the works it cites.
“Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks”
Gerrit Schoettler, Ashvin Nair, Juan Ojea, Sergey Levine and Eugen Solowjow · 2020
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“Offline Meta Learning of Exploration”
Ron Dorfman and Aviv Tamar · 2020
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Matej Vecerík, Todd Hester, Jonathan Scholz, Fumin Wang, Olivier Pietquin, Bilal Piot, Nicolas Heess, Thomas Rothörl, Thomas Lampe and Martin. Riedmiller · 2017
Cited alongside, same era.
“Deep Reinforcement Learning for High Precision Assembly Tasks”
Tadanobu Inoue, Giovanni Magistris, Asim Munawar, Tsuyoshi Yokoya and Ryuki Tachibana · 2017
Cited alongside, same era.
“Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks”
Chelsea Finn, Pieter Abbeel and Sergey Levine · 2017
Cited alongside, same era.
“One-shot visual imitation learning via meta-learning”
Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel and Sergey Levine · 2017
Cited alongside, same era.
“The Future of Manufacturing: A New Perspective”
Ben Wang · 2018
Cited alongside, same era.
“Learning Robotic Assembly from CAD”
Garrett Thomas, Melissa Chien, Aviv Tamar, Juan Ojea and P. Abbeel · 2018
Cited alongside, same era.
“Composable Deep Reinforcement Learning for Robotic Manipulation”
Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou, Murtaza Dalal, Pieter Abbeel and Sergey Levine · 2018
Cited alongside, same era.
“Meta-Reinforcement Learning of Structured Exploration Strategies”
Abhishek Gupta, R. Mendonca, Yuxuan Liu, P. Abbeel and Sergey Levine · 2018
Cited alongside, same era.
“IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data”
Ajay Mandlekar, Fabio Ramos, Byron Boots, Li Fei-Fei, Animesh Garg and Dieter Fox · 2020
Later among the works it cites.
“Offline Meta Reinforcement Learning”
Ron Dorfman and Aviv Tamar · 2020
Later among the works it cites.
URL: https://www.yahoo.com/now/global-robotics-market-growth-trends-125900343.html
“Global Robotics Market - Growth, Trends, COVID-19 Impact, and Forecasts (2021 - 2026)”, 2021 · 2021
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URL: https://blog.x.company/introducing-intrinsic-1cf35b87651
“Introducing Intrinsic”, 2021 · 2021
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URL: https://www.reuters.com/technology/alphabet-launch-robotics-firm-intrinsic-under-its-other-bets-unit-2021-07-23/
“Alphabet to launch robotics firm Intrinsic under its other bets unit”, 2021 · 2021
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“Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study”
Jianlan Luo, Oleg Sushkov, Rugile Pevceviciute, Wenzhao Lian, Chang Su, Mel Vecerik, Ning Ye, Stefan Schaal and Jonathan Scholz · 2021
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“A Minimalist Approach to Offline Reinforcement Learning”
Scott Fujimoto and Shixiang Gu · 2021
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“Offline Meta-Reinforcement Learning with Online Self-Supervision”, 2021
Vitchyr. Pong, Ashvin Nair, Laura Smith, Catherine Huang and Sergey Levine · 2021
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“Offline Meta-Reinforcement Learning with Advantage Weighting”
Eric Mitchell, Rafael Rafailov, Xue Peng, Sergey Levine and Chelsea Finn · 2021
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Lanqing Li, Rui Yang and Dijun Luo · 2021
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“InsertionNet - A Scalable Solution for Insertion”
O. Spector and Dotan Castro · 2021
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