# A Gesture-Based Teleoperation System for Compliant Robot Motion
## 1 Introduction  
With the development of space, ocean and atomic technology, there is an urgent need for robots to work in dangerous, uncertain environments and inaccessible workplaces [1]. Therefore, the teleoperation system of robots has received more and more attention. In industrial environments, many robotic tasks (e.g., assembly, grinding, painting, welding, etc.) require precise position and compliance control. Therefore, they asks the teleoperation systems not only to achieve precise positional control but also to have the function of force feedback. Teleoperation means the operator can remotely control the robot [2; 3; 4]. Usually, a physical human-robot interaction (pHRI) device is used to provide the motion commands [5], and such devices can be divided into joystick devices and motion-tracking devices. The joystick is usually a better control device because it can reflect forces that are experienced at the remote site [6]. For example, with Phantom [7] or Omega 7 [8], the contact force can be fed back to the operator. However, usually, the movement range is limited, and the mapping has to be tuned for different robots. The other way to teleoperate a robot is through a motion-tracking device. An operator can use his/her body to command the robot to move. There is no physical contact or constraints. Hand gestures are an effective way to teleoperate a robot. The hands' movements can be used to guide the robot to move directly. The static or dynamic gestures can also be mapped to different primitive robot actions. Gestures can be detected using different sensors, such as monocular cameras [9], stereo cameras [10], RGB-D sensors (i.e., Kinect, Xtion, RealSense) [11; 12] and sEMG sensors [13]. However, because of the limited sensing capability and accuracy, these devices can only be used to identify predefined gestures or track hands with low resolution. These facts limit the application of gesture-based human robot interaction. The leap motion (LM) sensor is a promising alternative to the above-mentioned sensors. It can track fingers, hands and even joints with an accuracy of up to 200 \(\upmu\)m [14]. Not only the position but also the rotation can be fed back in real-time at 60 fps [15]. It has been used to teleoperate different robots. [PERSON] developed a human-robot interaction system based on an LM sensor to operate a robot manipulator intuitively and adaptively [16]. [PERSON] used the LM sensor to track 3D hand motion and transfer the motion to an industrial robot [17]. [PERSON] developed a gesture-based non-contact teleoperation system to operate tabletop objects; both gestures and palm positions are used [18]. He also investigated the multiple LM configurations to improve the robustness of the hand tracking function [19]. [PERSON] used the joint angles and palm position to assist robotic surgical training [20]. The LM sensor and gesture-based human-robot interface have been successfully used in these applications. However, the gesture-based teleoperation technique has its own limitations. Firstly, since the motion of human arm/hand/limb motion is unstable and imprecise, it cannot be used to finish tight tolerant assembly skills. In some research, interval Kalman filter is used to filter the human motion [21]. However, the low-frequency movement still may affect the final robot motion. Secondly, although the LM is up to sub-millimeter accuracy, it still cannot meet the requirements of a tight tolerant assembly task where the error is in micrometer range. Thirdly, the LM has a limited sensing range, and it cannot be applied to tasks with large workspace; finally, like other non-contact interaction methods, the lack of force feedback makes it difficult to be applied in compliant tasks, which are common in complex assembly processes [22; 23]. Force feedback is an important module in a teleoperation system. The force feedback can be realized by haptic devices or vibrotactile devices.