Julian Ibarz
- Control and Systems Engineering top 5%
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Biomedical Engineering
- Automotive Engineering top 10%
- Co-authors
- Sergey LevineChelsea FinnPeter PástorMrinal KalakrishnanJie TanAlex IrpanKanishka RaoMohi Khansari
- Topics
- Reinforcement Learning in Robotics (7 papers)Robot Manipulation and Learning (5 papers)Multimodal Machine Learning Applications (2 papers)
- Cited by
- Control and Systems EngineeringArtificial IntelligenceComputer Vision and Pattern Recognition
- Journals
- The International Journal of Robotics ResearchIEEE Robotics and Automation LettersIEEE Transactions on Information Technology in Biomedicine
- Partner nations
- United StatesGermany
In The Last Decade
Julian Ibarz
11 papers receiving 619 citations
Hit Papers
Peers
Comparison fields: 5 of 78
- Control and Systems Engineering 337
- Artificial Intelligence 318
- Computer Vision and Pattern Recognition 142
- Biomedical Engineering 139
- Automotive Engineering 66
Countries citing papers authored by Julian Ibarz
This map shows the geographic impact of Julian Ibarz'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 Julian Ibarz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Julian Ibarz more than expected).
Fields of papers citing papers by Julian Ibarz
This network shows the impact of papers produced by Julian Ibarz. 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 Julian Ibarz. The network helps show where Julian Ibarz may publish in the future.
Co-authorship network of co-authors of Julian Ibarz
This figure shows the co-authorship network connecting the top 25 collaborators of Julian Ibarz. A scholar is included among the top collaborators of Julian Ibarz based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Julian Ibarz. Julian Ibarz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 108 | |
| 3 | How to train your robot with deep reinforcement learning: lessons we have learnedbreakdown → | 323 |
| 4 | 83 | |
| 5 | 1 | |
| 6 | Off-Policy Evaluation via Off-Policy Classification | 2 |
| 7 | QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation | 71 |
| 8 | Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning | 26 |
| 9 | End-to-End Learning of Semantic Grasping | 13 |
| 10 | 1 | |
| 11 | 3 |
About Julian Ibarz
Julian Ibarz is a scholar working on Artificial Intelligence, Control and Systems Engineering and Computer Vision and Pattern Recognition, having authored 11 papers that have together received 635 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (7 papers), Robot Manipulation and Learning (5 papers) and Multimodal Machine Learning Applications (2 papers). The work is most often cited by research in Control and Systems Engineering (337 citations), Artificial Intelligence (318 citations) and Computer Vision and Pattern Recognition (142 citations). Julian Ibarz has collaborated with scholars based in United States and Germany. Frequent co-authors include Sergey Levine, Chelsea Finn, Peter Pástor, Mrinal Kalakrishnan, Jie Tan, Alex Irpan, Kanishka Rao, Mohi Khansari, C.J. Harris and Michael Luo. Their work appears in journals such as The International Journal of Robotics Research, IEEE Robotics and Automation Letters and IEEE Transactions on Information Technology in Biomedicine.
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.