Mirco Theile


Address  

Chair of Cyber-Physical Systems in Production Engineering
School of Engineering and Design, Technical University of Munich
MW2107, Boltzmannstraße 15, 85748 Garching b. München

Phone  

+49 89 289 55175

Email  

mirco.theile@tum.de

Links  

        

Publications


Journals

Mirco Theile, Daniele Bernardini, Raphael Trumpp, Cristina Piazza, Marco Caccamo, Alberto L. Sangiovanni-Vincentelli,  "Learning to Generate All Feasible Actions",  in IEEE Access,  Mar 2024.

Binqi Sun, Mirco Theile, Ziyuan Qin, Daniele Bernardini, Debayan Roy, Andrea Bastoni, Marco Caccamo,  "Edge Generation Scheduling for DAG Tasks using Deep Reinforcement Learning",  in IEEE Transactions on Computers,  Vol. 73,  Issue 4,  Jan 2024.

O. Dantsker, M. Theile, and M. Caccamo,  "A Cyber-Physical Prototyping and Testing Framework to Enable the Rapid Development of UAVs",  in Aerospace,  Vol. 9,  No. 5,  May 2022.

H. Bayerlein, M. Theile, M. Caccamo, D. Gesbert,  "Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learning",  in IEEE Open Journal of the Communications Society (2021),  Vol. 2,  May 2021.

Conferences

Hongpeng Cao, Mirco Theile, Federico G Wyrwal, Marco Caccamo,  "Cloud-Edge Training Architecture for Sim-to-Real Deep Reinforcement Learning",  in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),  Oct 2022.

M. Theile, H. Bayerlein, R. Nai, D. Gesbert, M. Caccamo,  "UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning",  in 20th International Conference on Advanced Robotics (ICAR),  Ljubljana, Slovenia,  Dec 2021.

O. Dantsker, M. Theile, and M. Caccamo,  "Long Endurance Flight Testing Results for the UIUC-TUM Solar Flyer",  in AIAA Aviation and Aeronautics Forum and Exposition,  Virtual Forum,  Aug 2021.

O. Dantsker, M. Theile, M. Caccamo, and S. Hong,  "Integrated Power Simulation for a Solar-Powered, Computationally-Intensive Unmanned Aircraft",  in AIAA/IEEE Electric Aircraft Technologies Symposium,  Virtual Forum,  Aug 2021.

J. Ponniah, M. Theile, O. Dantsker, and M. Caccamo,  "Autonomous Hierarchical Multi-Level Clustering for Multi-UAV Systems",  in AIAA Scitech 2021 Forum,  Jan 2021.

H. Bayerlein, M. Theile, M. Caccamo, and D. Gesbert,  "UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach",  in Proceedings of IEEE Global Communications Conference (GLOBECOM),  Taipei, Taiwan,  Dec 2020.

M. Theile, H. Bayerlein, R. Nai, D. Gesbert, and M. Caccamo,  "UAV Coverage Path Planning under Varying Power Constraints using Deep Reinforcement Learning",  in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),  Las Vegas, Nevada, USA,  Oct 2020.

O. Dantsker, M. Theile, and M. Caccamo,  "Integrated Power Modeling for a Solar-Powered, Computationally-Intensive Unmanned Aircraft",  in AIAA/IEEE Electric Aircraft Technologies Symposium,  Virtual Forum,  Aug 2020.

M. Theile, O. Dantsker, R. Nai, M. Caccamo, and S. Yu,  "uavAP: A Modular Autopilot Framework for UAVs",  in AIAA Aviation and Aeronautics Forum and Exposition,  Virtual Forum,  Jun 2020.

M. Verucchi, M. Theile, M. Caccamo, and M. Bertogna,  "Latency-Aware Generation of Single-Rate DAGs from Multi-Rate Task Sets",  in Proceedings of 2020 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS),  Sydney, Australia,  Apr 2020.

O. Dantsker, M. Theile, M. Caccamo, S. Yu, M. Vahora, and R. Mancuso,  "Continued Development and Flight Testing of a Long-Endurance Solar-Powered Unmanned Aircraft: UIUC-TUM Solar Flyer",  in AIAA SciTech Forum,  Orlando, FL, USA,  Jan 2020.

M. Theile, S. Yu, O. Dantsker, and M. Caccamo,  "Trajectory Estimation for Geo-Fencing Applications on Small-Size Fixed-Wing UAVs",  in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),  Macau, China,  Nov 2019.

Preprints

Mirco Theile, Harald Bayerlein, Marco Caccamo, and Alberto Sangiovanni-Vincentelli,  "Learning to Recharge: UAV Coverage Path Planning through Deep Reinforcement Learning",  in submitted to IEEE Transactions on Neural Networks and Learning Systems,  .