  Understanding the intentions of human teammates is critical for safe and
effective human-robot interaction. The canonical approach for human-aware robot
motion planning is to first predict the human's goal or path, and then
construct a robot plan that avoids collision with the human. This method can
generate unsafe interactions if the human model and subsequent predictions are
inaccurate. In this work, we present an algorithmic approach for both arranging
the configuration of objects in a shared human-robot workspace, and projecting
``virtual obstacles'' in augmented reality, optimizing for legibility in a
given task. These changes to the workspace result in more legible human
behavior, improving robot predictions of human goals, thereby improving task
fluency and safety. To evaluate our approach, we propose two user studies
involving a collaborative tabletop task with a manipulator robot, and a
warehouse navigation task with a mobile robot.
