  We explore a novel method to perceive and manipulate 3D articulated objects
that generalizes to enable a robot to articulate unseen classes of objects. We
propose a vision-based system that learns to predict the potential motions of
the parts of a variety of articulated objects to guide downstream motion
planning of the system to articulate the objects. To predict the object
motions, we train a neural network to output a dense vector field representing
the point-wise motion direction of the points in the point cloud under
articulation. We then deploy an analytical motion planner based on this vector
field to achieve a policy that yields maximum articulation. We train the vision
system entirely in simulation, and we demonstrate the capability of our system
to generalize to unseen object instances and novel categories in both
simulation and the real world, deploying our policy on a Sawyer robot with no
finetuning. Results show that our system achieves state-of-the-art performance
in both simulated and real-world experiments.
