## 2 Related Work  
Gestural HRI is a widely researched topic across the whole spectrum of robotics. Allowing the robot to detect and recognize human movements and act accordingly is a powerful ability enabling the robot and the human worker to combine their strengths and achieve various challenging tasks. In mobile robotics, gestures can be used to direct and influence the robot's movement. [PERSON] and [PERSON] [7] used a mobile robotic platform, Pioneer 3-DX, and a Kinect sensor to recognize a human body and follow the operator's movement. [PERSON] et al. [6] proposed a method for controlling a mobile robotic platform, iRobot Create, equipped with a Kinect sensor via pointing gestures. [PERSON] et al. [8] used a Leap Motion Controller to control the movement of the mobile robotic platform with a robotic arm. Both the chassis and the robotic arm could be controlled via hand gestures. Gesture-based control was also used in projects RECO [9] and TeMoto [10], which focused on intuitive multimodal control of mobile robotic platforms. Besides mobile robotic platforms, force-compliant robotic manipulators are ideal for implementing various forms of gestural interaction, potentially deployable in multiple scenarios, such as object handling, assembly, assistance, etc. A significant part of the research is focused on teleoperation, in which the robot mirrors the human operator's movements. [PERSON] et al. [11] used a Leap Motion sensor and a collaborative manipulator UR10 to reproduce the movement of the operator's hands. [PERSON] et al. [12] proposed a similar approach using Leap Motion to control a dual-arm robot with both hands. They used the interval Kalman filter and improved particle filter methods to improve hand tracking. [PERSON] et al. [13] proposed a gesture control dual-arm telerobotic system, in which the operator controlled the position of an object held by the robotic system. Microsoft Kinect was used to track the human body. Many other researchers used Microsoft Kinect or a Leap Motion controller to control the robotic manipulator [14; 15; 16; 17; 18; 19; 20]. [PERSON] and [PERSON] [5] studied the feasibility of gesture control to replace conventional means of direct control through teach-pendant. [PERSON] et al. [21] proposed a gesture control for the delta architecture robot. [PERSON] and [PERSON] [22] investigated a gestural interface for teleoperating a robotic manipulator based on pointing gestures. Using stereo cameras, collineations, and an active contour model, they were able to pinpoint a precise location in the 40 cm workspace of the robot at which the user would point at with his finger. Object picking is another promising application area. A human hand can pick different objects of various sizes and shapes. Therefore, several works were conducted to mimic the human hand, to grasp objects [23; 24; 25]. An interesting concept was proposed by [PERSON] et al. [26], the authors proposed a gestural control for a concentric tube robot. The work focused on the potential use of such an interface in noninvasive surgical procedures. A gestural interface may be a promising concept in the control of UAV drones; some works [27; 28] show potential for future development. Social robotics is another vast research area where gestural interfaces could yield many benefits. Natural interfaces could be especially advantageous in interaction with humanoid robots. [PERSON] et al. [29] proposed a gestural control of the NAO humanoid robot. [PERSON] et al. [30] used this robot and a Kinect sensor to facilitate a gestural interaction between humans and the humanoid robot. In Table 1, there is a comparison of key works related to the interaction method designed by us. The vast majority focus mainly on teleoperation of the robotic manipulator used, while the user has visual feedback of the resulting robot motion. Only three approaches present some form of direct interaction of the operator with the manipulator's workspace and two of these allow the user to point and select objects present in the workspace.