  Robotics and automation are poised to change the landscape of home and work
in the near future. Robots are adept at deliberately moving, sensing, and
interacting with their environments. The pervasive use of this technology
promises societal and economic payoffs due to its capabilities - conversely,
the capabilities of robots to move within and sense the world around them is
susceptible to abuse. Robots, unlike typical sensors, are inherently
autonomous, active, and deliberate. Such automated agents can become AI double
agents liable to violate the privacy of coworkers, privileged spaces, and other
stakeholders. In this work we highlight the understudied and inevitable threats
to privacy that can be posed by the autonomous, deliberate motions and sensing
of robots. We frame the problem within broader sociotechnological questions
alongside a comprehensive review. The privacy-aware motion planning problem is
formulated in terms of cost functions that can be modified to induce
privacy-aware behavior - preserving, agnostic, or violating. Simulated case
studies in manipulation and navigation, with altered cost functions, are used
to demonstrate how privacy-violating threats can be easily injected, sometimes
with only small changes in performance (solution path lengths). Such
functionality is already widely available. This preliminary work is meant to
lay the foundations for near-future, holistic, interdisciplinary investigations
that can address questions surrounding privacy in intelligent robotic behaviors
determined by planning algorithms.
