Directly controlling robot motion through human–robot interaction enables the integration of human expertise into robot control. However, this requires efficient interaction methods. While previous work has shown that hand gestures offer potential for natural control, the lack of haptic feedback remains a key challenge. One solution is partial automation, in which the contact force is autonomously controlled. We implemented force automation enhanced gesture control, as well as the state-of-the-art baseline of hand-guiding, to systematically investigate hand gesture interaction for robot control. A user study (n=28) was conducted to investigate the efficiency and workload of these interaction concepts. As the interaction method efficiency may depend on task demands, two tasks with different precision requirements were evaluated in the context of robotic ultrasound. The results indicate that force automation significantly reduces perceived workload and task duration when using hand gestures. Moreover, gesture-based interaction can match the efficiency of hand-guiding for broad tasks and even outperform it in precise tasks. These findings highlight the potential of gesture-based interaction for robot control, particularly when the absence of haptic feedback is compensated by partial automation.

Robotic systems have the potential to enhance a wide range of domains, such as medical workflows, by automating individual steps of complex processes. However, human-robot interaction (HRI) is of critical importance, as effective collaboration between humans and robots is essential even in highly automated environments. Recent research has predominantly focused on the development of interaction methods rather than systematically comparing existing approaches. Therefore, we conducted a user study (n=20) to compare different HRI concepts for end effector manipulation combined with clutching mechanisms for manip-ulation activation in an alignment task using the example of robotic ultrasound (US). Manipulation methods included hand-guiding, teleoperation, and touchless interaction, while clutching mechanisms were realized through hand, voice, and foot interaction. The results indicate advantages of hand-guiding for manipulation. While no significant differences were observed between clutching mechanisms, strong evidence suggests comparable performance across these modalities. Notably, significant interaction effects on perceived workload reveal that the optimal clutching mechanism depends on the selected manipulation technique. This work underscores the critical importance of selecting appropriate HRI concepts and understanding the dependencies of manipulation techniques with clutching mechanisms. While our study included the usage of a robotic US, the insights gained are broadly transferable across various domains involving robotic manipulation tasks in human-robot collaboration.