The computational design of messenger RNA (mRNA) sequences is a critical technology for both scientific research and industrial applications. Recent advances in prediction and optimization models have enabled the automatic scoring and optimization of $5^\prime $ UTR sequences, key upstream elements of mRNA. However, fully automated design of $5^\prime $ UTR sequences with more than two objective scores has not yet been explored. In this study, we present a computational pipeline that optimizes human $5^\prime $ UTR sequences in a multi-objective framework, addressing up to four distinct and conflicting objectives. Our work represents an important advancement in the multi-objective computational design of mRNA sequences, paving the way for more sophisticated mRNA engineering.

Anticipating a future scenario where the robot initiates its own actions and behaves voluntarily when collaborating with humans, our research focuses on human understanding and perception of unanticipated robot actions during physical human-robot interaction. While the current literature searches for key factors that make the human-robot collaboration successful, the question of how people experience the robot’s unanticipated action as cooperative or uncooperative seems to remain open. We designed a game-based experiment (N = 35) where the participant played a “catch-falling-coins” game by moving a robotic arm. Our experiment introduced unanticipated robot actions in an “active session” where the robot targeted higher-valued coins without first informing the participants. Through semi-structured interviews and statistical analysis of questionnaires (Big Five Personality Test, SAM, NARS and CH33), we examined the participants’ understanding of the robot’s “intention” and their positive or negative perception of the robot as cooperative or uncooperative. Among the participants who understood that the robot’s “intention” was to catch the higher-valued coins, the majority of them reported a positive perception of the robot (cooperative or helpful) while this was not the case among those who did not understand the robot’s intention. We also observed relevant relationships between some personality traits and a person’s understanding of the robot’s intention. Qualitative analysis of the interviews allowed us to structure the process of perception change during the game into three phases: confusion, investigation, and adaptation. We believe that our research contributes to the study of human perception, and particularly to the relationship between a human’s understanding of unanticipated robot actions and their positive or negative perception of the robot.

While existing approaches to human-robot collaboration typically focus on how to build robots that can work safely and fluently with humans on collaborative tasks, our research focuses on how people experience interaction with a robot and interpret its behaviour as cooperative or uncooperative. A microsociological theory was used to analyse the process of interaction as it unfolds, aiming to examine human perception of the cooperativeness and uncooperativeness of a robot and identify the boundary between them in the context of human-robot collaboration. Our hypothesis was that an unexpected robot movement during human-robot interaction will cause a negative perception of uncooperativeness. An experiment where the interaction was ‘disrupted’ by the robot’s movement during a collaborative task was conducted with 21 participants. Our findings, obtained through qualitative analysis based on semi-structured interviews and observations, show that the disruption leads certainly to a negative perception of the robot. The perception of robot cooperativeness or uncooperativeness, however, includes complex processes, and its boundary is not rigid, but flexible and nuanced.

Unanticipated physical actions from the robot on humans [active physical human–robot interaction (pHRI)] may be inevitable with the deployment of robots in human-populated environments. However, it is still unclear how humans would perceive such actions and how the robot should execute them in a physically and psychologically safe manner. The objective of this article is to explore the possibility of quantifying the humans’ physical and mental state during an active physical interaction with a robot, by means of a laboratory experiment. We hypothesize that the active robot actions could cause measurable alterations in users’ data, which could be related to their perceptions and personalities. In the experiment, the user plays a visual game using the robot, which has a hidden task that results in active physical actions on the user. We collect data from physical and physiological sensors, and the perceptions and personalities via questionnaires and a semi-structured interview. Statistical analysis and clustering of the data collected from a total of 35 participants showed the relationships between participants’ physical and physiological data and their age, gender, perception, and personalities. Further developments based on these exploratory outcomes can be used to implement an active pHRI controller that can account for both the physical and the mental state of users.

While existing approaches to human-robot collaboration typically focus on how to build robots that can work safely and fluently with humans on collaborative tasks, our research focuses on how people experience interaction with a robot and interpret its behaviour as collaborative or non-collaborative. By applying a microsociological theory to analyse the process of interaction as it unfolds, our project aims to identify the boundary between cooperativeness and uncooperativeness of the robot in the shared task of a human and a robot carrying an object to a destination. The paper presents the experimental protocol designed based on the microsociological theory that is less known in current human-robot interaction studies. The aim of the experiment is to enable participants to experience expected and unexpected interactions with a robot and to account for how the interaction affects participant interpretation of robot cooperativeness and uncooperativeness.