Title: RMPflow: A Computational Graph for Automatic Motion Policy Generation,
Abstract: We develop a novel policy synthesis algorithm, RMPflow, based on
geometrically consistent transformations of Riemannian Motion Policies (RMPs).
RMPs are a class of reactive motion policies designed to parameterize
non-Euclidean behaviors as dynamical systems in intrinsically nonlinear task
spaces. Given a set of RMPs designed for individual tasks, RMPflow can
consistently combine these local policies to generate an expressive global
policy, while simultaneously exploiting sparse structure for computational
efficiency. We study the geometric properties of RMPflow and provide sufficient
conditions for stability. Finally, we experimentally demonstrate that
accounting for the geometry of task policies can simplify classically difficult
problems, such as planning through clutter on high-DOF manipulation systems.