Geometric Fabrics for the Acceleration-based Design of Robotic Motion
This work addresses the problem of designing stable and modular robotic behaviors for industry applications, representing a novel extension of existing methods rather than a completely new paradigm.
The paper tackles the design of reactive robotic motion by introducing geometric fabrics, a mathematical framework that generalizes Riemannian Motion Policies (RMPs) to provide provable stability guarantees and improve design consistency. The result is a system that enables intelligent global navigation behaviors without planning or state machines, demonstrated through practical industrial applications.
This paper describes the pragmatic design and construction of geometric fabrics for shaping a robot's task-independent nominal behavior, capturing behavioral components such as obstacle avoidance, joint limit avoidance, redundancy resolution, global navigation heuristics, etc. Geometric fabrics constitute the most concrete incarnation of a new mathematical formulation for reactive behavior called optimization fabrics. Fabrics generalize recent work on Riemannian Motion Policies (RMPs); they add provable stability guarantees and improve design consistency while promoting the intuitive acceleration-based principles of modular design that make RMPs successful. We describe a suite of mathematical modeling tools that practitioners can employ in practice and demonstrate both how to mitigate system complexity by constructing behaviors layer-wise and how to employ these tools to design robust, strongly-generalizing, policies that solve practical problems one would expect to find in industry applications. Our system exhibits intelligent global navigation behaviors expressed entirely as provably stable fabrics with zero planning or state machine governance.