Is a Robot a Better Walking Partner if it Associates Utterances with Visual Scenes?

We aim to develop a walking partner robot with the capability to select small-talk topics that are associative to visual scenes. We first collected video sequences from five different locations and prepared a dataset about small-talk topics associated to visual scenes. Then we developed a technique to associate the visual scenes with the small-talk topics. We converted visual scenes into lists of words using an off-the-shelf vision library and formed a topic space with a Latent Dirichlet Allocation (LDA) method in which a list of words is transformed to a topic vector. Finally, the system selects the most similar utterance in the topic vectors. We tested our developed technique with a dataset, which successfully selected 72% appropriate utterances, and conducted a user study outdoors where participants took a walk with a small robot on their shoulder and engaged in small talk. We confirmed that the participants more highly perceived the robot with our developed technique because it selected appropriate utterances than a robot that randomly selected utterances. Further, they also felt that the former type of robot is a better walking partner.