Space Autonomy Through the Lens of Foundation Models
Date: January 17, 2025 @ 3:30-4:00PM | Location: Gates B03 | Speaker: Daniele Gammelli | Affiliation: Stanford
Abstract:
Recent advances across multiple research fields are rapidly changing the way in which we develop autonomous systems. In this talk, I will discuss how space autonomy can benefit from the rise of foundation models. The discussion will focus on two perspectives. First, I will discuss how techniques that are traditional to the foundation model literature can be adapted for the purpose of reliable decision-making in space, with a focus on the application of Transformers for spacecraft trajectory optimization. Next, I will discuss the opportunities presented by pre-trained foundation models within future machine learning-based autonomy stacks for space applications, ranging from data curation to serving as reconfigurable automated reasoning modules within modular autonomy stacks, towards the goal of developing a broadly capable Space Foundation Model.
Bio:
Dr. Daniele Gammelli is a Postdoctoral Scholar in the Department of Aeronautics and Astronautics at Stanford University. He received his Ph.D. in Machine Learning and Mathematical Optimization at the Department of Technology, Management and Economics at the Technical University of Denmark. Dr. Gammelli’s research focuses on developing learning-based solutions that enable the deployment of future autonomous systems in complex environments, with an emphasis on large-scale robotic networks, aerospace systems, and future mobility systems. His research interests include deep reinforcement learning, imitation learning, generative models, graph representation learning, and control techniques leveraging these tools.