Robot Behavior-Based User Authentication for Motion-Controlled Robotic Systems

Motion-controlled robotic systems would become more and more popular in the future since they allow humans to easily control robots to carry out various tasks. However, current authentication methods rely on static credentials, such as passwords, fingerprints, and faces, which are independent of the robot control. Thus, they cannot guarantee that a robot is always under the control of its enrolled user. In this paper, we build a motion-controlled robotic arm system and show that a robotic arm’s motion inherits much of its user’s behavioral information in interactive control scenarios. Based on that, we propose a novel user authentication approach to verify the robotic arm user. In particular, we log the angle readings of the robotic arm’s joints to reconstruct the 3D movement trajectory of its end effector. We then develop a learning-based algorithm to identify the user. Extensive experiments show that our system achieves 95% accuracy to verify users while preventing various impersonation attacks.