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In this study, we focus on what perspectives people use to perceive a humanoid, and we explore the basic dimensions of a humanoid evaluation by ordinary people. We also develop the psychological scale called PERNOD (PERception to humaNOiD), a humanoid-oriented scale that is based on fundamental dimensions. Several studies have investigated the psychological evaluation of robots interaction with humans; however, almost studies have methodological limitations because few scales for the evaluation of psychological impressions of humanoids have been developed thus far. This study uses a humanoid of HRP-2 and explores what are the perspectives to evaluate the robot and investigate importance to develop humanoid-oriented scale. With free descriptions, 157 respondents provided impressions of a humanoid robot, and psychologists categorized the qualitative data into several groups according to psychological meanings of the impressions. Thereafter, the descriptions were organized in the form of a Likert scale in order to develop PERNOD, and 380 additional respondents evaluated a humanoid robot quantitatively using PERNOD. The result of factor analysis indicated that PERNOD comprises five basic dimensions for perceiving humanoid robots: Familiarity, Utility, Motion, Controllability, and Toughness. Some similarities are found in dimensions of evaluation for both humanoids and humans, and original dimensions are found in dimensions for humanoids. The reliability of each sub-scale of PERNOD is statistically high; further, the usability of this scale is discussed. [ABSTRACT FROM AUTHOR]

The ability of robots to understand human characteristics and make themselves socially accepted by humans are important issues if smooth collision avoidance between humans and robots is to be achieved. When discussing smooth collision avoidance, robot should understand not only physical components such as human position, but also social components such as body pose, face orientation and proxemics (personal space during motion). We integrated these components in a modified social force model (MSFM) which allows robots to predict human motion and perform smooth collision avoidance. In the modified model, short-term intended direction is described by body pose, and a supplementary force related face orientation is added for intention estimation. Face orientation is also the best indication of the direction of personal space during motion, which was verified in preliminary experiments. Our approach was implemented and tested on a real humanoid robot in a situation in which a human is confronted with the robot in an indoor environment. Experimental results showed that better human motion tracking was achieved with body pose and face orientation tracking. Being provided with the face orientation as an indication of the intended direction, and observing the laws of proxemics in a human-like manner, the robot was able to perform avoidance motions that were more human-like when compared to the original social force model (SFM) in a face-to-face confrontation. [ABSTRACT FROM AUTHOR]