Robots have come a long way in the past decade, but they still cannot reliably traverse or manipulate complex environments in the real world. This Faculty Early Career Development (CAREER) award supports research that looks to address two key reasons for this capability gap by developing 1) simulation tools that can efficiently and accurately capture relevant physics – including deformation, fluid interactions, and orbital dynamics – and 2) control methods that can reason about complex systems while simultaneously offering interpretability and safety guarantees. These advancements look to enable robots that can more efficiently, reliably, and safely interact with their environments and, therefore, bring us closer to a future in which robots are widely deployed to perform dangerous or tedious work. These robots could save lives by performing crucial tasks in dangerous environments instead of humans, and advance science by exploring places that are completely inaccessible to humans like deep oceans, space, and other planetary bodies. The education and outreach components of this award will also help inspire and recruit the next generation of scientists and engineers by directly engaging elementary and middle school students in future space missions. This project addresses two high-level technical goals. The first is to develop better modeling and simulation tools for situations in which sufficient data for reinforcement learning is too difficult or expensive to obtain on har