LEAF: Using Semantic Based Experience to Prevent Task Failures

Abstract

Using service robots at home is becoming more and more popular in order to help people in their life routine. Such robots are required to do various tasks, from user notification to devices manipulation. However, in such complex environments, robots sometimes fail to achieve one task. Failing is problematic as it is unpleasant for the user and may cause critical situations. Therefore, understanding and preventing failures is a challenging need. In this paper, we propose LEAF, an experience based approach to prevent task failure. LEAF relies on both semantic context knowledge through ontology and user validation, allowing LEAF to have an accurate understanding of failures. It then uses this new knowledge to adapt a Hierarchical Task Network (HTN) in order to prevent selecting tasks that have a high risk of failure in the plan. LEAF was tested in the Hadaptic platform and evaluated using a randomly generated dataset.