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dr. N. Botteghi (Nicolò)

Researcher

Expertise

Engineering & Materials Science
Mobile Robots
Navigation
Reinforcement Learning
Robotics
Robots
Earth & Environmental Sciences
Indoor Environment
Learning
Reinforcement

Publications

Recent
Botteghi, N., Grefte, L. , Poel, M. , Sirmacek, B. , Brune, C. , Dertien, E. , & Stramigioli, S. (2022). Towards Autonomous Pipeline Inspection with Hierarchical Reinforcement Learning. In J. Kim, B. Englot, H-W. Park, H-L. Choi, H. Myung, J. Kim, & J-H. Kim (Eds.), Robot Intelligence Technology and Applications 6 - Results from the 9th International Conference on Robot Intelligence Technology and Applications (pp. 259-271). (Lecture Notes in Networks and Systems; Vol. 429 LNNS). Springer. https://doi.org/10.1007/978-3-030-97672-9_23
Botteghi, N. , Sirmacek, B. , Poel, M. , Brune, C., & Schulte, R. (2021). CURIOSITY-DRIVEN REINFORCEMENT LEARNING AGENT for MAPPING UNKNOWN INDOOR ENVIRONMENTS. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 5(1), 129-136. https://doi.org/10.5194/isprs-annals-V-1-2021-129-2021
Botteghi, N., Alaa, K. , Poel, M. , Sirmaçek, B. , Brune, C., Mersha, A. , & Stramigioli, S. (2021). Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces. In IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021 (pp. 190-197). (IEEE International Conference on Intelligent Robots and Systems). IEEE. https://doi.org/10.1109/IROS51168.2021.9635936
Botteghi, N. (2021). Robotics deep reinforcement learning with loose prior knowledge. [PhD Thesis - Research UT, graduation UT, University of Twente]. University of Twente. https://doi.org/10.3990/1.9789036552165
Botteghi, N., Obbink, R., Geijs, D. , Poel, M. , Sirmacek, B. , Brune, C., Mersha, A. , & Stramigioli, S. (2021). Low Dimensional State Representation Learning with Reward-shaped Priors. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 3736-3743). [9412421] https://doi.org/10.1109/ICPR48806.2021.9412421

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Courses Academic Year  2022/2023

Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.
 

Contact Details

Visiting Address

University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling (building no. 11), room 2067
Hallenweg 19
7522NH  Enschede
The Netherlands

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Mailing Address

University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling  2067
P.O. Box 217
7500 AE Enschede
The Netherlands