Inferring Human Activities from Observation via Semantic Reasoning
Ramirez Amaro, Karinne · 2015
This thesis presents a framework that includes a new learning technique based on semantic representations that considers the re-usability and the inclusion of new skills in a robust manner. The obtained semantics extracts the essence of the observed activities in terms of human motions and object properties. The introduced framework has been assessed on a humanoid robot using different perceptual modalities, under different constraints and in several scenarios, demonstrating that our framework does not depend on the analyzed task.