Ajay Kumar Tanwani
- Control and Systems Engineering top 5%
- Artificial Intelligence top 10%
- Mechanical Engineering
- Biomedical Engineering
- Computer Vision and Pattern Recognition top 10%
- Co-authors
- Sylvain CalinonKen GoldbergPaweł WawrzyńskiJoseph E. GonzalezMuddassar FarooqDaniel SeitaNawid JamaliJohn Kubiatowicz
- Topics
- Robot Manipulation and Learning (13 papers)Reinforcement Learning in Robotics (7 papers)Modular Robots and Swarm Intelligence (4 papers)
- Cited by
- Control and Systems EngineeringHuman-Computer InteractionComputer Vision and Pattern Recognition
- Partner nations
- United StatesSwitzerlandGermany
In The Last Decade
Ajay Kumar Tanwani
26 papers receiving 490 citations
Peers
Comparison fields: 5 of 65
- Control and Systems Engineering 278
- Artificial Intelligence 130
- Mechanical Engineering 122
- Biomedical Engineering 116
- Computer Vision and Pattern Recognition 107
Countries citing papers authored by Ajay Kumar Tanwani
This map shows the geographic impact of Ajay Kumar Tanwani'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 Ajay Kumar Tanwani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ajay Kumar Tanwani more than expected).
Fields of papers citing papers by Ajay Kumar Tanwani
This network shows the impact of papers produced by Ajay Kumar Tanwani. 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 Ajay Kumar Tanwani. The network helps show where Ajay Kumar Tanwani may publish in the future.
Co-authorship network of co-authors of Ajay Kumar Tanwani
This figure shows the co-authorship network connecting the top 25 collaborators of Ajay Kumar Tanwani. A scholar is included among the top collaborators of Ajay Kumar Tanwani 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 Ajay Kumar Tanwani. Ajay Kumar Tanwani is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 12 | |
| 2 | 27 | |
| 3 | 72 | |
| 4 | 21 | |
| 5 | 5 | |
| 6 | 9 | |
| 7 | 6 | |
| 8 | 4 | |
| 9 | 19 | |
| 10 | 4 | |
| 11 | Robot Bed-Making: Deep Transfer Learning Using Depth Sensing of Deformable Fabric. | 10 |
| 12 | 45 | |
| 13 | Online Inference in Bayesian Non-Parametric Mixture Models under Small Variance Asymptotics | 3 |
| 14 | 57 | |
| 15 | 26 | |
| 16 | 8 | |
| 17 | 6 | |
| 18 | 41 | |
| 19 | 7 | |
| 20 | 29 |
About Ajay Kumar Tanwani
Ajay Kumar Tanwani is a scholar working on Control and Systems Engineering, Industrial and Manufacturing Engineering and Artificial Intelligence, having authored 26 papers that have together received 517 indexed citations. Recurring topics across this work include Robot Manipulation and Learning (13 papers), Reinforcement Learning in Robotics (7 papers) and Modular Robots and Swarm Intelligence (4 papers). The work is most often cited by research in Control and Systems Engineering (278 citations), Human-Computer Interaction (45 citations) and Computer Vision and Pattern Recognition (107 citations). Ajay Kumar Tanwani has collaborated with scholars based in United States, Switzerland and Germany. Frequent co-authors include Sylvain Calinon, Ken Goldberg, Paweł Wawrzyński, Joseph E. Gonzalez, Muddassar Farooq, Daniel Seita, Nawid Jamali, John Kubiatowicz, Soshi Iba and Ryan Hoque. Their work appears in journals such as The International Journal of Robotics Research, Neural Networks and Autonomous Robots.
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.