Olfactory stimuli have demonstrated the potential to evoke emotional depth and enhance user experiences in HCI. Yet, their role in shaping perceptions of social robots remains largely untapped. This study investigates how olfactory (scent) and auditory (voice) stimuli influence user perceptions of social robots. Using a 2x2 between-subjects design, participants interacted with a social robot under conditions with pleasant/unpleasant scents and friendly/unfriendly voices. The study measured perceived trust, friendliness, competence, and engagement. Our findings show that pleasant scents can enhance the perceptions of friendliness and engagement, while friendly voices can improve trust, friendliness, and engagement. The congruent combination of scents and voices affects friendliness and engagement but does not influence trust and competence. This study contributes to the growing work on multi-sensory Human-Robot Interaction (HRI) design, offering implications for creating more socially interactive robots.

Nowadays, social robots have become human's important companions. The anthropomorphic features of robots, which are important in building natural user experience and trustable human-robot partnership, have attracted increasing attention. Among these features, eyes attract most audience's attention and are particularly important. This study aims to investigate the influence of robot eye design on users’ trustworthiness perception. Specifically, a simulation robot model was developed. Three sets of experiments involving sixty-six participants were conducted to investigate the effects of (i) visual complexity of eye design, (ii) blink rate, and (iii) gaze aversion of social robots on users’ perceived trustworthiness. Results indicate that high visual complexity and gaze aversion lead to higher perceived trustworthiness and reveal a positive correlation between the perceived anthropomorphic effect of eye design and users’ perceived trust, while a non-significant effect of blink rate has been found. Preliminary suggestions are provided for the design of social robots in future works.

In order to realize visual servoing for robot manipulators, the robust and accurate estimation of a target object pose must be achieved. The pose estimation is achieved by a three-dimensional registration method. This paper presents this novel method for the registration of the rotated and translated part according to its camera images and three-dimensional model. The spatial registration parameters can be computed by finding which projection of the volume is the most similar to the given image. We propose a three-step procedure. First, we compute the Fourier transform (FT) of the volume projection by using FT projection theorem. Then, a cost function is designed according to the magnitude of projection FT and image FT. Last, an optimization algorithm called small world clonal selection algorithm (SWCSA) is applied to obtain spatial registration parameters. SWCSA combines the good local search ability in clonal selection algorithm and the random long-range search operator in small world, so the global optimization ability is enhanced. Experimental results demonstrate that the registration is accurate and robust.