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Understanding and manipulating articulated objects, such as doors anddrawers, is crucial for robots operating in human environments. We wish todevelop a system that can learn to articulate novel objects with no priorinteraction, after training on other articulated objects. Previous approachesfor articulated object manipulation rely on either modular methods which arebrittle or end-to-end methods, which lack generalizability. This paper presentsFlowBot++, a deep 3D vision-based robotic system that predicts dense per-pointmotion and dense articulation parameters of articulated objects to assist indownstream manipulation tasks. FlowBot++ introduces a novel per-pointrepresentation of the articulated motion and articulation parameters that arecombined to produce a more accurate estimate than either method on their own.Simulated experiments on the PartNet-Mobility dataset validate the performanceof our system in articulating a wide range of objects, while real-worldexperiments on real objects' point clouds and a Sawyer robot demonstrate thegeneralizability and feasibility of our system in real-world scenarios.

FlowBot++: Learning Generalized Articulated Objects Manipulation via Articulation Projection