A key goal in human-robot interaction (HRI) is to design scenarios between humanoid robots and humans such that the interaction is perceived as collaborative and natural, yet safe and comfortable for the human. Human skills like verbal and non-verbal communication are essential elements as humans tend to attribute social behaviors to robots. However, aspects like the uncanny valley and different technical affinity levels can impede the success of HRI scenarios, which has consequences on the establishment of long-term interaction qualities like trust and rapport. In the present study, we investigate the impact of a humanoid robot on human emotional responses during the performance of a cognitively demanding task. We set up three different conditions for the robot with increasing levels of social cue expressions in a between-group study design. For the analysis of emotions, we consider the eye gaze behavior, arousal-valence for affective states, and the detection of action units. Our analysis reveals that the participants display a high tendency toward positive emotions in presence of a robot with clear social skills compared to other conditions, where we show how emotions occur only at task onset. Our study also shows how different expression levels influence the analysis of the robots' role in HRI. Finally, we critically discuss the current trend of automatized emotion or affective state recognition in HRI and demonstrate issues that have direct consequences on the interpretation and, therefore, claims about human emotions in HRI studies.

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Designing a humanoid robot to assist in performing cognitive multitasking is not straightforward, as the robot’s communication itself could interfere with humans’ concentration on the tasks. Therefore, we focused on the social facilitation effect caused by the presence of social robots. According to our study, a social robot that expressed its willingness to collaborate in a positive manner and provided advice with social signals only in some tasks, improved the human performance even in another task where no direct advice was given. Moreover, the social robot reduced the mental workload measured by the integrated skin conductance response during the multitasking scenario. Our results showed a new approach to improve multitasking performance in a mixed team with humans and social humanoid robots.

The ability to estimate selfLposition accurately and robustly is widely regarded as one of the findarnental prcconditions to achieve next − generation car navigation and driver assistance systems ． The authors have proposed a localization technology that estimates self − position by matching the three −dirnensional map and entire circumference fish − eye cameras ' images with a particle filter ． It has been confrrmed that this technology can estimate self − position with high accuracy and strong robustness ifthe vehicle moves at low speed on a flat road ．In order to estimate selfLposition on the slopes and during high − speed moving ， the authors improve this technology to be able to set the scattering range of particles dynamically by referring to 出 e 血 g condition d 創 adient infbmlation ofthree − dimensional map ．

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