NealAI: A RAG-based Chat Assistant Supporting Command of a Robotic Team from the ISS

Abstract

We introduce NealAI, the first AI chat assistant to support astronauts with question answering during a space telerobotics experiment. In the Surface Avatar mission, an ISS crew member controlled a heterogeneous team of four robots in a simulated Martian environment. NealAI uses a Retrieval- Augmented Generation (RAG) approach, enabling a Large Lan- guage Model (LLM) to dynamically retrieve relevant context about the experiment and its robots, and deliver accurate, context-aware responses. To adhere to privacy requirements and computational costs, NealAI is based on a single small- scale LLM running locally. We assessed NealAI's performance in different evaluations, including a preliminary experiment with an ISS crew member teleoperating the robots, as well as a set of offline tests to evaluate the LLM context selection, the response correctness, and when (and why) hallucinations occur. Results demonstrate the feasibility and limitations of using a small-scale LLM on a RAG-based chat assistant during a space telerobotic experiment. Finally, we report some conclusions and lessons learned.