Advancing Aerospace Autonomy with Foundation Models

Foundation models, trained on vast and diverse data that capture broad aspects of the human experience, are at the heart of the ongoing AI revolution, transforming how we create, problem-solve, and work. These models??"and the insights gained from developing them??"are increasingly relevant to the advancement of autonomous robotic systems. In this talk, I will highlight how recent efforts to apply foundation models to embodied systems can be leveraged to push the frontiers of aerospace autonomy. The discussion will focus on two main thrusts. First, I will discuss how techniques traditionally developed in the foundation model literature can be adapted to enable reliable decision making in space, with an emphasis on spacecraft rendezvous and proximity operations. Next, I will discuss the opportunities presented by foundation models within data-driven autonomy stacks, ranging from automated data curation to serving as high-level reasoning modules.

Speaker: Daniele Gammelli, Stanford University

Room: 350-372