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The paradigm of learning-from-observation (LfO) enables a robot to learn howto perform actions by observing human-demonstrated actions. Previous researchin LfO have mainly focused on the industrial domain which only consist of theobservable physical constraints between a manipulating tool and the robot'sworking environment. In order to extend this paradigm to the household domainwhich consists non-observable constraints derived from a human's common sense;we introduce the idea of semantic constraints. The semantic constraints arerepresented similar to the physical constraints by defining a contact with animaginary semantic environment. We thoroughly investigate the necessary andsufficient set of contact state and state transitions to understand thedifferent types of physical and semantic constraints. We then apply ourconstraint representation to analyze various actions in top hit householdYouTube videos and real home cooking recordings. We further categorize thefrequently appearing constraint patterns into physical, semantic, andmultistage task groups and verify that these groups are not only necessary buta sufficient set for covering standard household actions. Finally, we conduct apreliminary experiment using textual input to explore the possibilities ofcombining verbal and visual input for recognizing the task groups. Our resultsprovide promising directions for incorporating common sense in the literatureof robot teaching.

Semantic constraints to represent common sense required in household actions for multi-modal Learning-from-observation robot