  A household robot is expected to perform various manipulative operations with
an understanding of the purpose of the task. To this end, a desirable robotic
application should provide an on-site robot teaching framework for non-experts.
Here we propose a Learning-from-Observation (LfO) framework for
grasp-manipulation-release class household operations (GMR-operations). The
framework maps human demonstrations to predefined task models through one-shot
teaching. Each task model contains both high-level knowledge regarding the
geometric constraints and low-level knowledge related to human postures. The
key idea is to design a task model that 1) covers various GMR-operations and 2)
includes human postures to achieve tasks. We verify the applicability of our
framework by testing an operational LfO system with a real robot. In addition,
we quantify the coverage of the task model by analyzing online videos of
household operations. In the context of one-shot robot teaching, the
contribution of this study is a framework that 1) covers various GMR-operations
and 2) mimics human postures during the operations.
