Robots with very human-like faces are expected to be able to display human-like facial expressions. However, manually defining the animations of such facial expressions can be intricate and, thus, methods to automatically animate the actuators of android robot heads are promising. An approach to learn a mapping from human faces to android actuator configurations in a self-supervised way is presented here. Compared to previous approaches, convincing results are achieved with relatively few samples of train data. A capable technology to automatically extract facial expressions from vision input is identified as an important requirement for such approaches. The learned mapping is compared to a manual one in a user study and both are rated mostly positive. In effect, combining both is likely to result in even better mimicry capabilities.

The android robot Andrea was set up at a public museum in Germany for six consecutive days to have conversations with visitors, fully autonomously. No specific context was given, so visitors could state their opinions regarding possible use-cases in structured interviews, without any bias. Additionally the 44 interviewees were asked for their general opinions of the robot, their reasons (not) to interact with it and necessary improvements for future use. The android’s voice and wig were changed between different days of operation to give varying cues regarding its gender. This did not have a significant impact on the positive overall perception of the robot. Most visitors want the robot to provide information about exhibits in the future, while opinions on other roles, like a receptionist, were both wanted and explicitly not wanted by different visitors. Speaking more languages (than only English) and faster response times were the improvements most desired. These findings from the interviews are in line with an analysis of the system logs, which revealed, that after chitchat and personal questions, most of the 4436 collected requests asked for information related to the museum and to converse in a different language. The valuable insights gained from these real-world interactions are now used to improve the system to become a useful real-world application.

This study investigates whether personalized voice cloning can improve a robot’s likability compared to a design-congruent voice and a distinctly dissimilar voice. Participants interacted with a gender-ambiguous android robot in three different voice conditions. We compared: (1) a personalized voice clone based on the participant’s voice, (2) a design-congruent voice matching the robot’s appearance, and (3) a dissimilar voice, which differs from both the participant’s and the robot’s features.The cloned and design-congruent voices significantly increased likability compared to the dissimilar voice, while anthropomorphism and familiarity showed no significant differences across conditions. Most participants did not immediately recognize their cloned voice until informed that one of the voices was a clone. However, most of the participants were successful when asked to pick out their cloned voice from those used. We assume that voice personalization through similarity to the user improves likability even before the user is aware of this similarity.Our results show that personalized voice cloning is a simple alternative to other methods for the design of robotic voices. It significantly increases robot likability while requiring minimal user effort.

We present two approaches to animate an android robot head according to audio speech input, both are adopted from recent machine learning based works in computer graphics animation. More concrete we implemented a viseme-based and a mesh-based approach on our robot. After a subjective comparison we conduct a speech-reading study to evaluate our preferred, the mesh-based, approach. The results show that on average the intelligibility is not increased by the visual cues provided through the robot head in comparison to noisy audio alone. This underlines the importance of carefully designing and controlling the facial co-speech movements of talking android heads.