Andrea Scorsoglio

PhD candidate at The University of Arizona. I focus on space applications of reinforcement learning, simulation and computer vision

Journal Papers

  • Scorsoglio, A., D’Ambrosio, A., Ghilardi, L., Gaudet, B., Curti, F., & Furfaro, R. (2021). Image-Based Deep Reinforcement Meta-Learning for Autonomous Lunar Landing. Journal of Spacecraft and Rockets, 1-13. DOI
  • 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, 171, 156-171. DOI
  • Holt, H., Armellin, R., Baresi, N., Hashida, Y., Turconi, A., Scorsoglio, A., & Furfaro, R. (2021). Optimal Q-laws via reinforcement learning with guaranteed stability. Acta Astronautica, 187, 511-528. DOI
  • Gaudet, B., Furfaro, R., Linares, R., & Scorsoglio, A. (2021). Reinforcement Metalearning for Interception of Maneuvering Exoatmospheric Targets with Parasitic Attitude Loop. Journal of Spacecraft and Rockets, 58(2), 386-399. DOI
  • Scorsoglio, A., Ghilardi, L., & Furfaro, R. (2023). A Physic-Informed Neural Network Approach to Orbit Determination. The Journal of the Astronautical Sciences, 70(4), 1-30. DOI
  • Scorsoglio, A., Furfaro, R., Linares, R., & Massari, M. (2023). Relative motion guidance for near-rectilinear lunar orbits with path constraints via actor-critic reinforcement learning. Advances in Space Research, 71(1), 316-335. DOI
  • Federici, L., Scorsoglio, A., Zavoli, A., & Furfaro, R. (2023). Autonomous Guidance Between Quasiperiodic Orbits in Cislunar Space via Deep Reinforcement Learning. Journal of Spacecraft and Rockets, 1-12. DOI
  • Pugliatti, M., Scorsoglio, A., Furfaro, R., & Topputo, F. (2023). Onboard State Estimation Around Didymos with Recurrent Neural Networks and Segmentation Maps. IEEE Transactions on Aerospace and Electronic Systems. DOI
  • Federici, L., Scorsoglio, A., Zavoli, A., & Furfaro, R. (2022). Meta-reinforcement learning for adaptive spacecraft guidance during finite-thrust rendezvous missions. Acta Astronautica, 201, 129-141. DOI
  • Federici, L., Scorsoglio, A., Ghilardi, L., D’Ambrosio, A., Benedikter, B., Zavoli, A., & Furfaro, R. (2022). Image-Based Meta-Reinforcement Learning for Autonomous Guidance of an Asteroid Impactor. Journal of Guidance, Control, and Dynamics, 45(11), 2013-2028.DOI

Conference Papers

  • Scorsoglio, A., Furfaro, R., Linares, R., & Massari, M. (2019). Actor-critic reinforcement learning approach to relative motion guidance in near-rectilinear orbit. 29th AAS/AIAA Space Flight Mechanics Meeting, 2019, Ka’anapali, Maui, HI, Advances in the Astronautical Sciences, 168, 1737-1756.PDF
  • Scorsoglio, A., & Furfaro, R. (2019). ELM-based Actor-Critic Approach to Lyapunov Vector Fields Relative Motion Guidance in Near-Rectilinear Orbit. In AAS/AIAA Astrodynamics Specialist Conference, 2019, Portland, ME. Advances in the Astronautical Sciences 171 (2019) (pp. 1-20). PDF
  • 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., D’Ambrosio, A., Ghilardi, L., Furfaro, R., Gaudet, B., Linares, R., & Curti, F. (2020, August). Safe Lunar landing via images: A Reinforcement Meta-Learning application to autonomous hazard avoidance and landing. In Proceedings of the 2020 AAS/AIAA Astrodynamics Specialist Conference, Virtual (pp. 9-12). PDF
  • Scorsoglio, A., & Furfaro, R (2021). "Orbit determination via physics informed neural networks." 31st AAS/AIAA Space Flight Mechanics Meeting, Virtual, 2021.
  • Scorsoglio, A., Ghilardi, L., & Furfaro, R. "Orbit determination via physics informed neural networks in cislunar environment." AAS/AIAA Astrodynamics Specialist Conference, Big Sky Virtual, 2021.
  • Scorsoglio, A., & Furfaro, R. (2021). VisualEnv: visual Gym environments with Blender. arXiv preprint arXiv:2111.08096. DOI
  • 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
  • Holt, H. J., Armellin, R., Baresi, N., Scorsoglio, A., & Furfaro, R. (2020). Low-thrust trajectory design using state-dependent closed-loop control laws and reinforcement learning. In 2020 AAS/AIAA Astrodynamics Specialist Conference. University of Surrey.
  • Furfaro, R., Drozd, K., Linares, R., Gaudet, B., & Scorsoglio, A. (2020). Deep imitation learning and clustering in astrodynamics. In AAS/AIAA Astrodynamics Specialist Conference, 2019 (pp. 3567-3584). Univelt Inc.
  • Ghilardi, L., D'Ambrosio, A., Scorsoglio, A., Furfaro, R., Linares, R., Curti, F. (2020), "Image-based Optimal Powered Descent Guidance via Deep Recurrent Imitation Learning", Conference: 2020 AAS/AIAA Astrodynamics Specialist Conference - Lake Tahoe (Virtual)
  • Schiassi, E., D’Ambrosio, A., Scorsoglio, A., Furfaro, R., & Curti, F. "Class of Optimal Space Guidance Problems Solved via Indirect Methods and Physics-Informed Neural Networks", Conference: 31st AAS/AIAA Space Flight Mechanics Meeting, Virtual PDF
  • Federici, L., Scorsoglio, A., Zavoli, A., & Furfaro, R. (2021). Autonomous Guidance for Cislunar Orbit Transfers via Reinforcement Learning. In AAS/AIAA Astrodynamics Specialist Conference. PDF
  • Federici, L., Scorsoglio, A., Zavoli, A., & Furfaro, R. (2021). Meta-Reinforcement Learning for Adaptive Spacecraft Guidance during Multi-Target Mission. In 72nd International Astronautical Congress (IAC) 2021.
  • Federici, L., Scorsoglio, A., Ghilardi, L., D'Ambrosio, A., Benedikter, B., & Furfaro, R. (2022). Image-Based Meta-Reinforcement Learning for Autonomous Terminal Guidance of an Impactor in a Binary Asteroid Systems. In AIAA SCITECH 2022 Forum (p. 2270). DOI
  • Furfaro, R., D'Ambrosio, A., Schiassi, E., & Scorsoglio, A. (2022). Physics-Informed Neural Networks for Closed-Loop Guidance and Control in Aerospace Systems. In AIAA SCITECH 2022 Forum (p. 0361).