Level:
Master
Contact:
Damian Hostettler
External Research Assistant
With increasing importance of robots in both private and business contexts, a deep understanding of what makes human-robot interactions satisfactory and how the ergonomics can be further improved is of critical importance. Since the number of industrial and collaborative robots is rising rapidly and since cobots are built for close collaborations with humans and require fewer safety precautions, the optimized design of interactions becomes even more important.
However, existing research in the HRI field focuses mostly on appearance and behavior of humanoid and social robots with human characteristics such as gaze, mimics, or voice. Other than humanoid robots, industrial robots have limited human-like characteristics and cannot interact through facial expressions or verbal communication. Since we are unable to modulate such characteristics for industrial cobots, it is critical to advance our understanding on how other factors that we can modulate (e.g., robot arm movements) influence their acceptance.
Using modern technologies such as emotion recognition, eye tracking or personality traits detection based on individual mobile app adoption might enable that limited devices like a robotic arm not only choose autonomously between certain behaviors but also detect relevant human characteristics in real time and adapt their behavior accordingly.
First findings support the hypothesis that specific movement behaviors of an industrial robot influence perceived human-likeness and that human-likeness has a systematic subsequent effect on
humans' preferences. To further investigate these effects you will conduct lab experiments and test how different robot movements affect user perception, including moderating user characteristics.