Intuitive Multi-modal Human-Robot Interaction via Posture and Voice

Collaborative robots promise to greatly improve the quality-of-life for the aging population and also easing elder care. However existing systems often rely on hand gestures, which can be restrictive and less accessible for users with cognitive disability. This paper introduces a multi-modal command input, which combines voice and deictic postures, to create a natural humanrobot interaction. In addition, we combine our system with a chatbot to make the interaction responsive. The demonstrated deictic postures, voice and the perceived table-top scene are processed in real-time to extract the human's intention. The system is evaluated for increasingly complex tasks using a real Universal Robots UR3e 6-DoF robot arm. The preliminary results demonstrate a high success rate in task completion and a notable improvement compared to gesture-based systems. Controlling robots through multi-modal commands, as opposed to gesture control, can save up to 48.1% of the time taken to issue commands to the robot. Our system adeptly integrates the advantages of voice commands and deictic postures to facilitate intuitive human-robot interaction. Compared to conventional gesture control methods, our approach requires minimal training, eliminating the need to memorize complex gestures, and results in shorter interaction times.