Analysis of implicit robot control methods for joint task execution

Abstract

Abstract Body language is an essential component of communication. The amount of unspoken information it transmits during interpersonal interactions is an invaluable complement to simple speech and makes the process smoother and more sustainable. On the contrary, existing approaches to human–machine collaboration and communication are not as intuitive. This is an issue that needs to be addressed if we aim to continue using artificial intelligence and machines to increase our cognitive or even physical capabilities. In this study, we analyse the potential of an intuitive communication method between biological and artificial agents, based on machines understanding and learning the subtle unspoken and involuntary cues found in human motion during the interaction process. Our work was divided into two stages: the first, analysing whether a machine using these implicit cues would produce the same positive effect as when they are manifested in interpersonal communication; the second, evaluating whether a machine could identify the cues manifested in human motion and learn (through the use of Long-Short Term Memory Networks) to associate them with the appropriate command intended from its user. Promising results were gathered, showing an improved work performance and reduced cognitive load on the user side when relying on the proposed method, hinting to the potential of more intuitive, human to human inspired, communication methods in human–machine interaction.

Keywords

• Human-robot interaction
• Human–machine cooperation
• Machine learning
• Implicit control