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Updated: Nov 16, 2025

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Security and privacy issues related to the workplace-based security robot system.

Zhang Guangnan1, Hai Tao1,2, Md Arafatur Rahman3

  • 1School of Computer Science, Baoji University of Arts and Sciences, Baoji, China.

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Robots interacting with people need to understand human uncertainty. This research proposes using probabilistic planning and system theory for robots to reason about human states, enhancing security robot systems.

Keywords:
Inner statecomplex robotmodern algorithmsspectrum

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Human-Robot Interaction

Background:

  • Robots operating in human environments must manage uncertainty in their world models.
  • Robot Interaction with Security and Privacy Issues (RISAPI) requires robots to infer and adapt to human internal states and their dynamics.

Purpose of the Study:

  • To present original work on RISAPI, demonstrating how probabilistic planning and system theory can enable reasoning in robotic systems.
  • To frame the problem of robot interaction with humans as an incomplete knowledge 2-player game.

Main Methods:

  • Application of probabilistic planning algorithms.
  • Utilization of system theory principles.
  • Modeling robot-human interaction as a 2-player game with incomplete knowledge.

Main Results:

  • Developed a general framework for robot reasoning about human states.
  • Demonstrated complex robot behaviors through real-time interaction.
  • Showcased the transformation of human subjects within production systems, robots, and care facilities.

Conclusions:

  • Current models for robot understanding of human internal states are limited and computationally challenging.
  • Versatile, lightweight representations of human internal states are crucial for efficient robot design.
  • Advanced algorithms hold promise for enabling robots to reason effectively about human states.