Safe, reliable navigation in extreme, unfamiliar terrain is required for future robotic space exploration missions. Recent generative-AI methods learn semantically aware navigation policies from large, cross-embodiment datasets, but offer limited safety guarantees. Inspired by human cognitive science, we propose a risk-guided diffusion framework that fuses a fast, learned "System-1" with a slow, physics-based "System-2", sharing computation at both training and inference to couple adaptability with formal safety. Hardware experiments conducted at the NASA JPL's Mars-analog facility, Mars Yard, show that our approach reduces failure rates by up to 4× while matching the goal-reaching performance of learning-based robotic models by leveraging inference-time compute without any additional training.
@article{arxiv.2506.17601,
title = {Risk-Guided Diffusion: Toward Deploying Robot Foundation Models in Space, Where Failure Is Not An Option},
author = {Rohan Thakker and Adarsh Patnaik and Vince Kurtz and Jonas Frey and Jonathan Becktor and Sangwoo Moon and Rob Royce and Marcel Kaufmann and Georgios Georgakis and Pascal Roth and Joel Burdick and Marco Hutter and Shehryar Khattak},
journal= {arXiv preprint arXiv:2506.17601},
year = {2025}
}