  It is crucial that users are empowered to take advantage of the functionality
of a robot and use their understanding of that functionality to perform novel
and creative tasks. Given a robot trained with Reinforcement Learning (RL), a
user may wish to leverage that autonomy along with their familiarity of how
they expect the robot to behave to collaborate with the robot. One technique is
for the user to take control of some of the robot's action space through
teleoperation, allowing the RL policy to simultaneously control the rest. We
formalize this type of shared control as Partitioned Control (PC). However,
this may not be possible using an out-of-the-box RL policy. For example, a
user's control may bring the robot into a failure state from the policy's
perspective, causing it to act unexpectedly and 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 which
empowers users to leverage their expectations of a robot's behavior to
accomplish new tasks. We deploy IODA in a user study with a real robot and find
that IODA leads to both better task performance and a higher degree of
alignment between robot behavior and user expectation. We also show that in PC,
there is a strong and significant correlation between task performance and the
robot's ability to meet user expectations, highlighting the need for approaches
like IODA. Code is available at https://github.com/AABL-Lab/ioda_roman_2024
