  Assistive robot arms enable people with disabilities to conduct everyday
tasks on their own. These arms are dexterous and high-dimensional; however, the
interfaces people must use to control their robots are low-dimensional.
Consider teleoperating a 7-DoF robot arm with a 2-DoF joystick. The robot is
helping you eat dinner, and currently you want to cut a piece of tofu. Today's
robots assume a pre-defined mapping between joystick inputs and robot actions:
in one mode the joystick controls the robot's motion in the x-y plane, in
another mode the joystick controls the robot's z-yaw motion, and so on. But
this mapping misses out on the task you are trying to perform! Ideally, one
joystick axis should control how the robot stabs the tofu and the other axis
should control different cutting motions. Our insight is that we can achieve
intuitive, user-friendly control of assistive robots by embedding the robot's
high-dimensional actions into low-dimensional and human-controllable latent
actions. We divide this process into three parts. First, we explore models for
learning latent actions from offline task demonstrations, and formalize the
properties that latent actions should satisfy. Next, we combine learned latent
actions with autonomous robot assistance to help the user reach and maintain
their high-level goals. Finally, we learn a personalized alignment model
between joystick inputs and latent actions. We evaluate our resulting approach
in four user studies where non-disabled participants reach marshmallows, cook
apple pie, cut tofu, and assemble dessert. We then test our approach with two
disabled adults who leverage assistive devices on a daily basis.
