Aravind Rajeswaran
- Electrical and Electronic Engineering
- Control and Systems Engineering top 5%
- Artificial Intelligence top 10%
- Safety, Risk, Reliability and Quality top 5%
- Biomedical Engineering
- Co-authors
- Ramkrishna PasumarthyNirav BhattSergey LevineVikash KumarAbhishek GuptaChelsea FinnSham M. KakadeEmanuel Todorov
- Topics
- Reinforcement Learning in Robotics (9 papers)Robot Manipulation and Learning (7 papers)Domain Adaptation and Few-Shot Learning (4 papers)
- Cited by
- Safety, Risk, Reliability and QualityControl and Systems EngineeringArtificial Intelligence
- Journals
- IEEE Transactions on Smart GridarXiv (Cornell University)Neural Information Processing Systems
- Partner nations
- United StatesIndia
In The Last Decade
Aravind Rajeswaran
13 papers receiving 430 citations
Peers
Comparison fields: 5 of 42
- Electrical and Electronic Engineering 212
- Control and Systems Engineering 174
- Artificial Intelligence 141
- Safety, Risk, Reliability and Quality 69
- Biomedical Engineering 69
Countries citing papers authored by Aravind Rajeswaran
This map shows the geographic impact of Aravind Rajeswaran'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 Aravind Rajeswaran with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aravind Rajeswaran more than expected).
Fields of papers citing papers by Aravind Rajeswaran
This network shows the impact of papers produced by Aravind Rajeswaran. 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 Aravind Rajeswaran. The network helps show where Aravind Rajeswaran may publish in the future.
Co-authorship network of co-authors of Aravind Rajeswaran
This figure shows the co-authorship network connecting the top 25 collaborators of Aravind Rajeswaran. A scholar is included among the top collaborators of Aravind Rajeswaran 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 Aravind Rajeswaran. Aravind Rajeswaran is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 3 | |
| 4 | 10 | |
| 5 | 4 | |
| 6 | MOReL: Model-Based Offline Reinforcement Learning | 2 |
| 7 | A Game Theoretic Framework for Model Based Reinforcement Learning | 6 |
| 8 | 50 | |
| 9 | 24 | |
| 10 | 100 | |
| 11 | 24 | |
| 12 | Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines | 5 |
| 13 | 190 | |
| 14 | 33 |
About Aravind Rajeswaran
Aravind Rajeswaran is a scholar working on Control and Systems Engineering, Artificial Intelligence and Safety, Risk, Reliability and Quality, having authored 14 papers that have together received 452 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (9 papers), Robot Manipulation and Learning (7 papers) and Domain Adaptation and Few-Shot Learning (4 papers). The work is most often cited by research in Safety, Risk, Reliability and Quality (69 citations), Control and Systems Engineering (174 citations) and Artificial Intelligence (141 citations). Aravind Rajeswaran has collaborated with scholars based in United States and India. Frequent co-authors include Ramkrishna Pasumarthy, Nirav Bhatt, Sergey Levine, Vikash Kumar, Abhishek Gupta, Chelsea Finn, Sham M. Kakade, Emanuel Todorov, Vikash Kumar and Igor Mordatch. Their work appears in journals such as IEEE Transactions on Smart Grid, arXiv (Cornell University) and Neural Information Processing Systems.
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.