A household robot is expected to perform a wide variety of manipulating 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 by non-experts. Here we propose to apply the Learning-from-Observation (LfO) framework to grasp-manipulation-release class household operations  . The framework maps a human demonstration to predefined task models through a one-shot demonstration. A task model contains both high-level knowledge about the geometric constraints of tasks and low-level knowledge about human postures. The key idea is to design a task model that 1) covers a wide variety of household operations and 2) contains human postures to achieve tasks. We verified the effectiveness of our framework by testing an implemented system with several operations . In addition, we quantified the coverage of the task model by analyzing Web videos about household operations. In the context of one-shot robot teaching, the contributions of the paper is to propose a framework that 1) covers various tasks in grasp-manipulation-release class household operations and 2) mimics human postures during the operations .
A household robot is expected to perform various manipulative operations with an understanding of the purpose of the task. To this end, robotic applications 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 of tasks and low-level knowledge related to human postures. The key goal of this study 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 the novel LfO system with a real robot . In addition, we quantify the coverage of the task model by analyzing online videos of household operations. Within the context of one-shot robot teaching, the contribution of this study is a framework that covers various GMR-operations and mimics human postures during operation .
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