  Manipulating an articulated object requires perceiving itskinematic
hierarchy: its parts, how each can move, and howthose motions are coupled.
Previous work has explored per-ception for kinematics, but none infers a
complete kinematichierarchy on never-before-seen object instances, without
relyingon a schema or template. We present a novel perception systemthat
achieves this goal. Our system infers the moving parts ofan object and the
kinematic couplings that relate them. Toinfer parts, it uses a point cloud
instance segmentation neuralnetwork and to infer kinematic hierarchies, it uses
a graphneural network to predict the existence, direction, and typeof edges
(i.e. joints) that relate the inferred parts. We trainthese networks using
simulated scans of synthetic 3D models.We evaluate our system on simulated
scans of 3D objects, andwe demonstrate a proof-of-concept use of our system to
drivereal-world robotic manipulation.
