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We develop a novel policy synthesis algorithm, RMPflow, based ongeometrically consistent transformations of Riemannian Motion Policies (RMPs).RMPs are a class of reactive motion policies designed to parameterizenon-Euclidean behaviors as dynamical systems in intrinsically nonlinear taskspaces. Given a set of RMPs designed for individual tasks, RMPflow canconsistently combine these local policies to generate an expressive globalpolicy, while simultaneously exploiting sparse structure for computationalefficiency. We study the geometric properties of RMPflow and provide sufficientconditions for stability. Finally, we experimentally demonstrate thataccounting for the geometry of task policies can simplify classically difficultproblems, such as planning through clutter on high-DOF manipulation systems.

RMPflow: A Computational Graph for Automatic Motion Policy Generation