Increasing the Acceptance of Industrial Robots by Adapting Movement Behavior to Individual User Differences

Machines, computers, and robots play an increasingly important role in our personal and professional lives. In manufacturing industries, the direct physical collaboration between workers and robots is already reality today. To understand how humans perceive this collaboration, investigations regarding the effect of different robot behaviors and the characteristics that influence the perception by humans are required. Existing findings on the effect of human attributes and human-like appearance of robots point out several possibilities that improve interactions between humans and robots. However, these studies predominantly focus on humanoid robots. With regard to the increasing number of industrial robots and the arising economic potential of automation, the objective of my research is to gain in-depth understanding of human perception of robot movement behaviors. Achieving this requires insights on possible and perceptible robot behaviors in the non-humanoid field, how they are perceived and how they can be mapped to acceptance by humans. Based on this understanding, resulting interactions can be improved by adapting relevant behavior to the individual's preferences. In view of new technologies and possibilities, it might even be possible to use real time data to classify humans and let robots adapt their behavior automatically.