  Pose estimation is a basic module in many robot manipulation pipelines.
Estimating the pose of objects in the environment can be useful for grasping,
motion planning, or manipulation. However, current state-of-the-art methods for
pose estimation either rely on large annotated training sets or simulated data.
Further, the long training times for these methods prohibit quick interaction
with novel objects. To address these issues, we introduce a novel method for
zero-shot object pose estimation in clutter. Our approach uses a hypothesis
generation and scoring framework, with a focus on learning a scoring function
that generalizes to objects not used for training. We achieve zero-shot
generalization by rating hypotheses as a function of unordered point
differences. We evaluate our method on challenging datasets with both textured
and untextured objects in cluttered scenes and demonstrate that our method
significantly outperforms previous methods on this task. We also demonstrate
how our system can be used by quickly scanning and building a model of a novel
object, which can immediately be used by our method for pose estimation. Our
work allows users to estimate the pose of novel objects without requiring any
retraining. Additional information can be found on our website
https://bokorn.github.io/zephyr/
