Andrea Scorsoglio

Andrea Scorsoglio was born in Piacenza, Italy. He has earned his bachelor's and master's degrees at Politecnico di Milano in aerospace engineering and space engineering respectively. He developed his master thesis in 2017/2018 within a collaboration between Politecnico di Milano and The University of Arizona where he then started his Ph.D. in System Engineering in 2019. Since summer 2019, he is working on the NASA mission named Near Earth Objects Surveillance Mission (NEOSM) as part of the Follow-Up working group, his main contribution being the development of a Near-Earth Objects simulator to be used to support and validate a ranking algorithm needed for decision making related to NEOs follow-up strategies. His research is mainly focused on reinforcement learning for spacecraft guidance applications with visual environments.

Degrees

• MSc, Space Engineering, Politecnico di Milano (2018)
• BSc, Aerospace Engineering, Politecnico di Milano (2016)

Publications

• Gaudet, B., Furfaro, R., Linares, R., & Scorsoglio, A. (2020). Reinforcement Meta-Learning for Interception of Maneuvering Exoatmospheric Targets with Parasitic Attitude Loop. arXiv preprint arXiv:2004.09978. PDF
• Scorsoglio, A., & Furfaro, R. (2019). ELM-based Actor-Critic Approach to Lyapunov Vector Fields Relative Motion Guidance in Near-Rectilinear Orbit. In 2019 AAS/AIAA Astrodynamics Specialists Conference (pp. 1-20). PDF
• Furfaro, R., Scorsoglio, A., Linares, R., & Massari, M. (2020). Adaptive generalized ZEM-ZEV feedback guidance for planetary landing via a deep reinforcement learning approach. Acta Astronautica. DOI: http://dx.doi.org/10.1016/j.actaastro.2020.02.051
• Holt, H., Armellin, R., Scorsoglio, A., & Furfaro, R. (2020). Low-Thrust Trajectory Design Using Closed-Loop Feedback-Driven Control Laws and State-Dependent Parameters. In AIAA Scitech 2020 Forum (p. 1694). DOI: https://doi.org/10.2514/6.2020-1694
• Scorsoglio, A., Furfaro, R., Linares, R., & Gaudet, B. (2020). Image-based Deep Reinforcement Learning for Autonomous Lunar Landing. In AIAA Scitech 2020 Forum (p. 1910). DOI: http://dx.doi.org/10.2514/6.2020-1910
• Scorsoglio, A., Furfaro, R., Linares, R., & Massari, M. (2019). Actor-critic reinforcement learning approach to relative motion guidance in near-rectilinear orbit. In 29th AAS/AIAA Space Flight Mechanics Meeting (pp. 1-20).  PDF
• Scorsoglio, A., & Furfaro, R. (2019), ELM-based Actor-Critic Approach to Lyapunov Vector Fields Relative Motion Guidance in Near-Rectilinear Orbits. Conference: 2019 AAS/AIAA Astrodynamics Specialist Conference. Portland, ME, USA. PDF

Conference Presentations

• 30th AIAA/AAS Space Flight Mechanics Meeting/AIAA SciTech Forum and Exposition, Orlando, Florida (USA), 6-10 January 2020, Image-based Deep Reinforcement Learning for Autonomous Landing.
• AAS/AIAA Astrodynamics Specialist Conference, Portland, Maine (USA), 11-15 August 2019. ELM-based Actor-Critic approach to Lyapunov Vector Fields Relative Motion Guidance in Near-Rectilinear Orbits.
• 29th AIAA/AAS Space Flight Mechanics Meeting, Ka'anapali, Hawaii (USA), 13-17 January 2019. Actor-critic reinforcement learning approach to relative motion guidance in near-rectilinear orbit.