Calibrated Decision Models for Onboard Spacecraft Autonomy

Institut

Lehrstuhl für Spacecraft Systems (TUM-ED)

Typ

Masterarbeit /

Inhalt

 

Beschreibung

An autonomous spacecraft has to decide, minute by minute, whether to charge, observe, process data, downlink or stay safe. Generative LLM agents can take such decisions from a textual state description, but each answer is slow, costly and carries no trustworthy statement of its own reliability. System One decision models instead answer typed questions in a single forward pass and return a calibrated probability for every option. Trustworthy confidence could decide when the spacecraft acts on its own and when it defers to a safer controller or to the ground. Building on our work on autonomous satellite decision-making, the decision-model branch of our AUTOPS framework already connects the open-weight Kev model family (0.8B to 27B parameters) to an Orekit-based simulator of the EventSat 6U CubeSat. First zero-shot closed-loop runs fly complete simulated weeks and expose the open problems this thesis attacks: the energy cost of the decision cadence, a possible sensitivity to option order, confidence that is not yet calibrated for the mission, and a clear gap to a rule-based scheduler. The student works with the AUTOPS codebase, a data-centre GPU, an NVIDIA Jetson Orin platform and close supervision towards a joint publication.

Voraussetzungen

• Excellent academic record and the ambition to do publishable research
• Solid machine learning background; hands-on experience with PyTorch and transformer
• language models (e.g. LoRA fine-tuning) is a strong plus
• Very good Python and software engineering skills (Git, testing, Linux, remote GPU servers)
• Good command of probability and statistics (calibration, uncertainty, bootstrap)
• Basic knowledge of spacecraft operations (power, attitude, communication); experience with
  imitation learning, reinforcement learning or planning is a plus

Möglicher Beginn

sofort

Kontakt

Clemente Juan Oliver
clemente.juantum.de

Ausschreibung