Stanford Seminar - Space Autonomy Through the Lens of Foundation Models

January 17, 2025 Daniele Gammelli, Student at Stanford University 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. About the speaker: https://danielegammelli.github.io/ More about the course can be found here: https://stanfordasl.github.io/robotics_seminar/ View the entire AA289 Stanford Robotics and Autonomous Systems Seminar playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMeercb-kvGLUrOq4HR6BZD ► Check out the entire catalog of courses and programs available through Stanford Online: https://online.stanford.edu/explore View our Robotics and Autonomous Systems Graduate Certificate: https://online.stanford.edu/programs/robotics-and-autonomous-systems-graduate-certificate