This paper presents an architecture dedicated to the orchestration of high level abilities of a humanoid robot, such as a Pepper, which must perform some tasks as the ones proposed in the RoboCup@Home competition. We present the main abilities that a humanoid service robot should provide. We choose to build them based on recent methodologies linked to social navigation and deep learning. We detail the architecture, on how high level abilities are connected with low level sub-functions. Finally we present first experimental results with a Pepper humanoid.

In order to perform tasks and offer socially acceptable humanrobot interactions, domestic robots need the ability to collect various information about people. In this paper, we propose a framework that allows the extraction of high-level person features from a 2D camera in addition to tracking people over time. The proposed people management framework aggregates body and person features including an original pose estimation using only a 2D camera. At this time, people pose and posture, clothing colors, face recognition are combined with tracking and re-identification abilities. This framework has been successfully used by the LyonTech team in the RoboCup@Home 2018 competition with a Pepper robot from SoftBank Robotics where its utility for domestic robot applications was demonstrated.