  It is crucial that users are empowered to use the functionalities of a robot
to creatively solve problems on the fly. A user who has access to a
Reinforcement Learning (RL) based robot may want to use the robot's autonomy
and their knowledge of its behavior to complete new tasks. One way is for the
user to take control of some of the robot's action space through teleoperation
while the RL policy simultaneously controls the rest. However, an
out-of-the-box RL policy may not readily facilitate this. For example, a user's
control may bring the robot into a failure state from the policy's perspective,
causing it to act in a way the user is not familiar with, hindering the success
of the user's desired task. In this work, we formalize this problem and present
Imaginary Out-of-Distribution Actions, IODA, an initial algorithm for
addressing that problem and empowering user's to leverage their expectation of
a robot's behavior to accomplish new tasks.
