Anticipating the Start of User Interaction for Service Robot in the Wild

A service robot is expected to provide proactive service for visitors who require its help. In contrast to passive service, e.g., providing service only after being spoken to, proactive service initiates an interaction at an early stage, e.g., talking to potential visitors who need the robot’s help in advance. This paper addresses how to anticipate the start of user interaction. We propose an approach using only a single RGB camera that anticipates whether a visitor will come to the robot for interaction or just pass it by. In the proposed approach, we (i) utilize the visitor’s pose information from captured images incorporating facial information, (ii) train a CNN-LSTM–based model in an end-to-end manner with an exponential loss for early anticipation, and (iii) during the training, the network branch for facial keypoints acquired as the part of the human pose information is taught to mimic the branch trained with the face image from a specialized face detector with a human verification. By virtue of (iii), at the inference, we can run our model in an embedded system processing only the pose information without an additional face detector and typical accuracy drop.We evaluated the proposed approach on our collected real world data with a real service robot and publicly available JPL interaction dataset and found that it achieved accurate anticipation performance.