  When humans control robot arms these robots often need to infer the human's
desired task. Prior research on assistive teleoperation and shared autonomy
explores how robots can determine the desired task based on the human's
joystick inputs. In order to perform this inference the robot relies on an
internal mapping between joystick inputs and discrete tasks: e.g., pressing the
joystick left indicates that the human wants a plate, while pressing the
joystick right indicates a cup. This approach works well after the human
understands how the robot interprets their inputs -- but inexperienced users
still have to learn these mappings through trial and error! Here we recognize
that the robot's mapping between tasks and inputs is a convention. There are
multiple, equally efficient conventions that the robot could use: rather than
passively waiting for the human, we introduce a shared autonomy approach where
the robot actively reveals its chosen convention. Across repeated interactions
the robot intervenes and exaggerates the arm's motion to demonstrate more
efficient inputs while also assisting for the current task. We compare this
approach to a state-of-the-art baseline -- where users must identify the
convention by themselves -- as well as written instructions. Our user study
results indicate that modifying the robot's behavior to reveal its convention
outperforms the baselines and reduces the amount of time that humans spend
controlling the robot. See videos of our user study here:
https://youtu.be/jROTVOp469I
