A Supervised Learning Architecture for Human Pose Recognition in a Social Robot
Abstract
A main activity of Social Robots is to interact with people. To do that, the robot must be able to understand what the user is saying or doing. This document presents a supervised learning architecture to enable a social robot to recognise human poses. The architecture is trained using data obtained from a depth camera that allows the creation of a kinematic model of the user. The user labels each set of poses by telling it directly to the robot, which identifies these labels with an Automatic Speech Recognition System (ASR). The architecture is evaluated with two different datasets where the quality of the training examples varies. In both datasets, a user trains the classifier to recognise three different poses. The learned classifiers are evaluated against twelve different users demonstrating high accuracy and robustness when representative examples are provided in the training phase. Using this architecture in a social robot might improve the quality of the human-robot interaction since the robot is able to detect non-verbal cues from the user, making the robot more aware of the interaction context.
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