Robots as AI Double Agents: Privacy in Motion Planning

TL;DR

This paper explores the privacy risks posed by autonomous robots through motion planning, demonstrating how their deliberate movements can threaten privacy and proposing a framework to control privacy-aware behaviors.

Contribution

It introduces a formal framework for privacy-aware motion planning in robots, highlighting potential privacy violations and demonstrating how small changes in planning can induce privacy breaches.

Findings

Privacy-violating behaviors can be induced with minimal changes in robot motion plans.

Simulated case studies show the feasibility of privacy violations in manipulation and navigation tasks.

The framework enables modification of robot behavior to preserve, ignore, or violate privacy.

Abstract

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.