Toward Verifying the User of Motion-Controlled Robotic Arm Systems via the Robot Behavior

Abstract

Motion-controlled robotic arms 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. In this work, we build up a motion-controlled robotic arm framework comprising a robotic arm end and a user end, which are connected via a network and responsible for manipulator control and motion capture, respectively. To protect the system access, we propose to verify who is controlling the robotic arm by examining the robotic arm’s behavior, which adds a second security layer in addition to the system login credentials. We show that a robotic arm’s motion inherits its human controller’s behavioral biometric in interactive control scenarios. By extracting the angle readings of the robotic arm’s all joints, the proposed user authentication approach reconstructs the robotic arm’s end-effector movement trajectory that follows the user’s hand. 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 to be one of the enrolled users or a nonuser. Extensive experiments show that our system achieves 94% accuracy to distinguish users while preventing user identity spoofing attacks with 95% accuracy.