  Understanding and manipulating articulated objects, such as doors and
drawers, is crucial for robots operating in human environments. We wish to
develop a system that can learn to articulate novel objects with no prior
interaction, after training on other articulated objects. Previous approaches
for articulated object manipulation rely on either modular methods which are
brittle or end-to-end methods, which lack generalizability. This paper presents
FlowBot++, a deep 3D vision-based robotic system that predicts dense per-point
motion and dense articulation parameters of articulated objects to assist in
downstream manipulation tasks. FlowBot++ introduces a novel per-point
representation of the articulated motion and articulation parameters that are
combined to produce a more accurate estimate than either method on their own.
Simulated experiments on the PartNet-Mobility dataset validate the performance
of our system in articulating a wide range of objects, while real-world
experiments on real objects' point clouds and a Sawyer robot demonstrate the
generalizability and feasibility of our system in real-world scenarios.
