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.

翻译：机器人与自动化技术即将在不久的将来改变家庭与工作环境的面貌。机器人擅长有意地移动、感知并与环境交互。这类技术的广泛应用因其能力而有望带来社会与经济效益——但反之，机器人在周围世界中移动和感知的能力也容易被滥用。与典型传感器不同，机器人本质上是自主、主动且有意图的。这类自动化智能体可能成为AI双重间谍，倾向于侵犯同事、特权空间及其他利益相关者的隐私。本研究着重强调了机器人自主、有意的运动与感知可能对隐私构成的、尚未得到充分研究且不可避免的威胁。我们将该问题置于更广泛的社会技术问题背景下进行阐述，并结合全面文献综述加以分析。隐私感知运动规划问题被表述为可通过修改成本函数来诱导隐私感知行为（包括保护、中立或侵犯行为）的框架。通过修改成本函数，在操作与导航领域的仿真案例研究表明：隐私侵犯威胁可轻易被注入，有时仅需对性能（解路径长度）进行微小改动。此类功能已广泛可用。这项初步工作旨在为未来跨学科综合研究奠定基础，以解决由规划算法决定的机器人智能行为中的隐私相关问题。