Extracting human behavioral biometrics from robot motions

Motion-controlled robots allow a user to interact with a remote real world without physically reaching it. By connecting cyberspace to the physical world, such interactive teleoperations are promising to improve remote education, virtual social interactions and online participatory activities. This work builds up a motion-controlled robotic arm framework and proposes to verify who is controlling the robotic arm by examining the robotic arm's behavior. We show that a robotic arm's motion inherits its human controller's behavioral biometric in interactive control scenarios. Furthermore, we derive the unique robotic motion features to capture the user's behavioral biometric embedded in the robot motions and develop learning-based algorithms to verify the robotic arm user. Extensive experiments show that our system achieves high accuracy to distinguish users while using the robot's behaviors.