  Classical mechanical systems are central to controller design in energy
shaping methods of geometric control. However, their expressivity is limited by
position-only metrics and the intimate link between metric and geometry. Recent
work on Riemannian Motion Policies (RMPs) has shown that shedding these
restrictions results in powerful design tools, but at the expense of
theoretical stability guarantees. In this work, we generalize classical
mechanics to what we call geometric fabrics, whose expressivity and theory
enable the design of systems that outperform RMPs in practice. Geometric
fabrics strictly generalize classical mechanics forming a new physics of
behavior by first generalizing them to Finsler geometries and then explicitly
bending them to shape their behavior while maintaining stability. We develop
the theory of fabrics and present both a collection of controlled experiments
examining their theoretical properties and a set of robot system experiments
showing improved performance over a well-engineered and hardened implementation
of RMPs, our current state-of-the-art in controller design.
