RESUMO
In recent years, various service robots have been deployed in stores as recommendation systems. Previous studies have sought to increase the influence of these robots by enhancing their social acceptance and trust. However, when such service robots recommend a product to customers in real environments, the effect on the customers is influenced not only by the robot itself, but also by the social influence of the surrounding people such as store clerks. Therefore, leveraging the social influence of the clerks may increase the influence of the robots on the customers. Hence, we compared the influence of robots with and without collaborative customer service between the robots and clerks in two bakery stores. The experimental results showed that collaborative customer service increased the purchase rate of the recommended bread and improved the impressions of the robot and store experience of the customers. Because the results also showed that the workload required for the clerks to collaborate with the robot was not high, this study suggests that all stores with service robots may demonstrate high effectiveness in introducing collaborative customer service.
RESUMO
Research is currently being conducted on the use of robots as human labor support technology. In particular, the service industry needs to allocate more manpower, and it will be important for robots to support people. This study focuses on using a humanoid robot as a social service robot to convey information in a shopping mall, and the types of robot behaviors were analyzed. In order to convey the information, two processes must occur. Pedestrians must stop in front of the robot, and the robot must continue the engagement with them. For the purpose of this study, three types of autonomous robot behaviors were analyzed and compared in these processes in the experiment: greeting, in-trouble, dancing behaviors. After interactions were attempted with 5,000+ pedestrians, this study revealed that the in-trouble behavior can make pedestrians stop more and stay longer. In addition, in order to evaluate the effectiveness of the robot in a real environment, the comparative results between three robot behaviors and human advertisers revealed that (1) the results of the greeting and dancing behavior are comparable to those of the humans, and (2) the performance of the in-trouble behavior in providing information tasks is higher than that of all human advertisers. These findings demonstrate that the performance of robots is comparable to that of humans in providing information tasks in a limited environment; therefore, it is expected that service robots as a labor support technology will be able to perform well in the real world.
RESUMO
Driver analysis, particularly revealing where drivers gaze, is a key factor in understanding drivers' perception. Several studies have examined drivers' gaze behavior and the two main hypotheses that have been developed are Tangent Point (TP) and Future Path Point (FP). TP is a point on the inner side of the lane, where the driver's gaze direction becomes tangential with the lane edge. FP is an arbitrary single point on the ideal future path for an individual driver on the road. The location of this single point is dependent on the individual driver. While these gaze points have been verified and discussed by various psychological experiments, it is unclear why drivers gaze at these points. Therefore, in this study, we used optical flow theory to understand drivers' gaze strategy. Optical flow theory is a method to quantify the extent to which drivers can perceive the future path of the vehicle. The results of numerical simulations demonstrated that optical flow theory can potentially estimate drivers' gaze behavior. We also conducted an experiment in which the observed driver gaze behavior was compared to calculated gaze strategy based on optical flow theory. The experimental results demonstrate that drivers' gaze can be estimated with an accuracy of 70.8% and 65.1% on circular and straight paths, respectively. Thus, these results suggest that optical flow theory can be a determining factor in drivers' gaze strategy.
RESUMO
Successful driving involves steering corrections that respond to immediate positional errors while also anticipating upcoming changes to the road layout ahead. In popular steering models these tasks are often treated as separate functions using two points: the near region for correcting current errors, and the far region for anticipating future steering requirements. Whereas two-point control models can capture many aspects of driver behavior, the nature of perceptual inputs to these two "points" remains unclear. Inspired by experiments that solely focused on road-edge information (Land & Horwood, 1995), two-point models have tended to ignore the role of optic flow during steering control. There is recent evidence demonstrating that optic flow should be considered within two-point control steering models (Mole, Kountouriotis, Billington, & Wilkie, 2016). To examine the impact of optic flow and road edges on two-point steering control we used a driving simulator to selectively and systematically manipulate these components. We removed flow and/or road-edge information from near or far regions of the scene, and examined how behaviors changed when steering along roads where the utility of far-road information varied. While steering behaviors were strongly influenced by the road-edges, there were also clear contributions of optic flow to steering responses. The patterns of steering were not consistent with optic flow simply feeding into two-point control; rather, the global optic flow field appeared to support effective steering responses across the time-course of each trajectory.
