Alignment of the attitude of teleoperators with that of a semi-autonomous android

Studies on social robots that can communicate with humans are increasingly important. In particular, semi-aautonomous robots have shown potential for practical applications in which robot autonomy and human teleoperation are jointly used to accomplish difficult tasks. However, it is unknown how the attitude represented in the autonomous behavior of the robots affects teleoperators. Previous studies reported that when humans play a particular role, their attitudes align with that role. The teleoperators of semi-autonomous robots also play the role given to the robots and may assimilate their autonomous expression. We hypothesized that the attitude of teleoperators may align with that of robots through teleoperation. To verify this, we conducted an experiment with conditions under which a participant operated a part of the body of an android robot that autonomously expressed a preferential attitude toward a painting and a condition under which they did not. Experimental results demonstrated that the preferential attitude of participants who teleoperated the android aligned statistically significantly more with that of the robot in comparison to those who did not teleoperate it, thereby supporting our hypothesis. This finding is novel regarding attitude change in teleoperators of semi-autonomous robots and can support the implementation of effective human-robot collaboration systems.

Information Content of Prefrontal Cortex Activity Quantifies the Difficulty of Narrated Stories

The ability to realize the individuals' impressions during the verbal communication allows social robots to significantly facilitate their social interactions in such areas as child education and elderly care. However, such impressions are highly subjective and internalized and therefore cannot be easily comprehended through behavioural observations. Although brain-machine interface suggests the utility of the brain information in human-robot interaction, previous studies did not consider its potential for estimating the internal impressions during verbal communication. In this article, we introduce a novel approach to estimation of the individuals' perceived difficulty of stories using the quantified information content of their prefrontal cortex activity. We demonstrate the robustness of our approach by showing its comparable performance in face-to-face, humanoid, speaker, and video-chat settings. Our results contribute to the field of socially assistive robotics by taking a step toward enabling robots determine their human companions' perceived difficulty of conversations, thereby enabling these media to sustain their communication with humans by adapting to individuals' pace and interest in response to conversational nuances and complexity.

Multiscale Entropy Quantifies the Differential Effect of the Medium Embodiment on Older Adults Prefrontal Cortex during the Story Comprehension: A Comparative Analysis

Todays’ communication media virtually impact and transform every aspect of our daily communication and yet the extent of their embodiment on our brain is unexplored. The study of this topic becomes more crucial, considering the rapid advances in such fields as socially assistive robotics that envision the use of intelligent and interactive media for providing assistance through social means. In this article, we utilize the multiscale entropy (MSE) to investigate the effect of the physical embodiment on the older people’s prefrontal cortex (PFC) activity while listening to stories. We provide evidence that physical embodiment induces a significant increase in MSE of the older people’s PFC activity and that such a shift in the dynamics of their PFC activation significantly reflects their perceived feeling of fatigue. Our results benefit researchers in age-related cognitive function and rehabilitation who seek for the adaptation of these media in robot-assistive cognitive training of the older people. In addition, they offer a complementary information to the field of human-robot interaction via providing evidence that the use of MSE can enable the interactive learning algorithms to utilize the brain’s activation patterns as feedbacks for improving their level of interactivity, thereby forming a stepping stone for rich and usable human mental model.