Steven Bohez
- Artificial Intelligence top 5%
- Neural Networks and Applications 4
- Reinforcement Learning in Robotics 3
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- Advanced Neural Network Applications 6
- Human Pose and Action Recognition 3
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- Robot Manipulation and Learning 4
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- IoT and Edge/Fog Computing 10
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- Cloud Computing and Resource Management 4
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- Modular Robots and Swarm Intelligence 2
- Co-authors
- Jie TanVincent VanhouckeErwin CoumansAtıl IşçenDanijar HafnerTingnan ZhangYunfei BaiPieter Simoens
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionControl and Systems Engineering
- Journals
- Future Generation Computer Systems (1 paper)Journal of Systems and Software (2 papers)Multimedia Tools and Applications (1 paper)
- Partner nations
- BelgiumUnited KingdomUnited States
In The Last Decade
Steven Bohez
26 papers receiving 730 citations
Hit Papers
Peers
Comparison fields: 5 of 75
- Artificial Intelligence 321
- Computer Vision and Pattern Recognition 199
- Control and Systems Engineering 211
- Computer Networks and Communications 175
- Biomedical Engineering 217
Countries citing papers authored by Steven Bohez
This map shows the geographic impact of Steven Bohez'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 Steven Bohez with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Steven Bohez more than expected).
Fields of papers citing papers by Steven Bohez
This network shows the impact of papers produced by Steven Bohez. 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 Steven Bohez. The network helps show where Steven Bohez may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Steven Bohez, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 12 | |
| 2 | A Constrained Multi-Objective Reinforcement Learning Framework | 2021 | 2 |
| 3 | 2020 | 89 | |
| 4 | 2019 | 10 | |
| 5 | Robot navigation using a variational dynamics model for state estimation and robust control | 2018 | 1 |
| 6 | Sim-to-Real: Learning Agile Locomotion For Quadruped Robotsbreakdown → | 2018 | 379 |
| 7 | Success at any cost: value constrained model-free continuous control | 2018 | 1 |
| 8 | 2018 | 21 | |
| 9 | 2017 | 37 | |
| 10 | 2017 | 13 | |
| 11 | 2016 | 23 | |
| 12 | 2016 | 1 | |
| 13 | 2016 | 9 | |
| 14 | 2016 | 2 | |
| 15 | 2015 | 1 | |
| 16 | 2015 | 12 | |
| 17 | 2015 | 22 | |
| 18 | 2014 | 2 | |
| 19 | 2014 | 25 | |
| 20 | 2013 | 17 |
About Steven Bohez
Steven Bohez is a scholar working on Computer Vision and Pattern Recognition, Computer Networks and Communications and Control and Systems Engineering, having authored 26 papers that have together received 749 indexed citations. Recurring topics across this work include IoT and Edge/Fog Computing (10 papers), Advanced Neural Network Applications (6 papers), Robot Manipulation and Learning (4 papers), Neural Networks and Applications (4 papers), Cloud Computing and Resource Management (4 papers), Human Pose and Action Recognition (3 papers), Reinforcement Learning in Robotics (3 papers) and Modular Robots and Swarm Intelligence (2 papers). The work is most often cited by research in Artificial Intelligence (321 citations), Computer Vision and Pattern Recognition (199 citations) and Control and Systems Engineering (211 citations). Steven Bohez has collaborated with scholars based in Belgium, United Kingdom and United States. Frequent co-authors include Jie Tan, Vincent Vanhoucke, Erwin Coumans, Atıl Işçen, Danijar Hafner, Tingnan Zhang, Yunfei Bai, Pieter Simoens, Bart Dhoedt and Tim Verbelen. Their work appears in journals such as Future Generation Computer Systems, Journal of Systems and Software and Multimedia Tools and Applications.
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.