Sergey Levine
Impact in
- Control and Systems Engineering top 0.02%
- Robot Manipulation and Learning
- Human Motion and Animation
- Artificial Intelligence top 0.02%
- Reinforcement Learning in Robotics
- Domain Adaptation and Few-Shot Learning
Papers in
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- Reinforcement Learning in Robotics 163
- Domain Adaptation and Few-Shot Learning 12
- AI-based Problem Solving and Planning 6
- Topic Modeling 5
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- Robot Manipulation and Learning 61
- Human Motion and Animation 15
- Co-authors
- Pieter Abbeel (51 shared papers)Chelsea Finn (39 shared papers)Peter Pástor (4 shared papers)Gregory Kahn (12 shared papers)Jitendra Malik (10 shared papers)Xue Bin Peng (12 shared papers)Vladlen Koltun (12 shared papers)Trevor Jackson Darrell (10 shared papers)
- Journals
- ACM Transactions on Graphics (10 papers)IEEE Robotics and Automation Letters (9 papers)The International Journal of Robotics Research (4 papers)IEEE Transactions on Robotics (2 papers)Philosophical Transactions of the Royal Society B Biological Sciences (1 paper)
- Partner nations
- United StatesCanadaUnited Kingdom
In The Last Decade
Sergey Levine
227 papers receiving 21k citations
Sergey Levine's Hit Papers
Peers
Comparison fields: 5 of 143
- Control and Systems Engineering 8.4k
- Artificial Intelligence 11.2k
- Computer Vision and Pattern Recognition 2.4k
- Automotive Engineering 810
- Biomedical Engineering 1.8k
Countries citing papers authored by Sergey Levine
This map shows the geographic impact of Sergey Levine's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sergey Levine with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sergey Levine more than expected).
Fields of papers citing papers by Sergey Levine
This network shows the impact of papers produced by Sergey Levine. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sergey Levine. The network helps show where Sergey Levine may publish in the future.
Co-authors
The 25 scholars most cited alongside Sergey Levine, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 243 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection Hit paper breakdown → | 2017 | 1416 |
| 2 | Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates Hit paper breakdown → | 2017 | 1027 |
| 3 | End-to-end training of deep visuomotor policies Hit paper breakdown → | 2016 | 900 |
| 4 | Recurrent Network Models for Human Dynamics Hit paper breakdown → | 2015 | 668 |
| 5 | DeepMimic Hit paper breakdown → | 2018 | 626 |
| 6 | Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations Hit paper breakdown → | 2018 | 522 |
| 7 | Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning Hit paper breakdown → | 2018 | 506 |
| 8 | CAD2RL: Real Single-Image Flight Without a Single Real Image Hit paper breakdown → | 2017 | 469 |
| 9 | Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping Hit paper breakdown → | 2018 | 439 |
| 10 | RT-1: Robotics Transformer for Real-World Control at Scale Hit paper breakdown → | 2023 | 416 |
| 11 | How to train your robot with deep reinforcement learning: lessons we have learned Hit paper breakdown → | 2021 | 401 |
| 12 | Time-Contrastive Networks: Self-Supervised Learning from Video Hit paper breakdown → | 2018 | 397 |
| 13 | Deep visual foresight for planning robot motion Hit paper breakdown → | 2017 | 384 |
| 14 | Cognitive Mapping and Planning for Visual Navigation Hit paper breakdown → | 2017 | 367 |
| 15 | Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware Hit paper breakdown → | 2023 | 338 |
| 16 | Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search Hit paper breakdown → | 2016 | 318 |
| 17 | 2019 | 297 | |
| 18 | 2019 | 295 | |
| 19 | 2019 | 293 | |
| 20 | 2017 | 290 |
About Sergey Levine
Sergey Levine is a scholar working on Artificial Intelligence, Control and Systems Engineering, Computer Vision and Pattern Recognition, Biomedical Engineering and Automotive Engineering, having authored 243 papers that have together received 21.6k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (163 papers), Robot Manipulation and Learning (61 papers), Human Motion and Animation (15 papers), Robotic Locomotion and Control (15 papers), Domain Adaptation and Few-Shot Learning (12 papers), AI-based Problem Solving and Planning (6 papers), Topic Modeling (5 papers) and Autonomous Vehicle Technology and Safety (5 papers). The work is most often cited by research in Control and Systems Engineering (8.4k citations), Artificial Intelligence (11.2k citations), Computer Vision and Pattern Recognition (2.4k citations), Automotive Engineering (810 citations) and Biomedical Engineering (1.8k citations). Sergey Levine has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Pieter Abbeel, Chelsea Finn, Peter Pástor, Gregory Kahn, Jitendra Malik, Xue Bin Peng, Vladlen Koltun, Trevor Jackson Darrell, Alex Krizhevsky and Shixiang Gu. Their work appears in journals such as ACM Transactions on Graphics, IEEE Robotics and Automation Letters, The International Journal of Robotics Research, IEEE Transactions on Robotics and Philosophical Transactions of the Royal Society B Biological Sciences.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.