  Robots should personalize how they perform tasks to match the needs of
individual human users. Today's robot achieve this personalization by asking
for the human's feedback in the task space. For example, an autonomous car
might show the human two different ways to decelerate at stoplights, and ask
the human which of these motions they prefer. This current approach to
personalization is indirect: based on the behaviors the human selects (e.g.,
decelerating slowly), the robot tries to infer their underlying preference
(e.g., defensive driving). By contrast, our paper develops a learning and
interface-based approach that enables humans to directly indicate their desired
style. We do this by learning an abstract, low-dimensional, and continuous
canonical space from human demonstration data. Each point in the canonical
space corresponds to a different style (e.g., defensive or aggressive driving),
and users can directly personalize the robot's behavior by simply clicking on a
point. Given the human's selection, the robot then decodes this canonical style
across each task in the dataset -- e.g., if the human selects a defensive
style, the autonomous car personalizes its behavior to drive defensively when
decelerating, passing other cars, or merging onto highways. We refer to our
resulting approach as PECAN: Personalizing Robot Behaviors through a Learned
Canonical Space. Our simulations and user studies suggest that humans prefer
using PECAN to directly personalize robot behavior (particularly when those
users become familiar with PECAN), and that users find the learned canonical
space to be intuitive and consistent. See videos here:
https://youtu.be/wRJpyr23PKI
