A Learning-from-Observation Framework: One-Shot Robot Teaching for Grasp-Manipulation-Release Household Operations
Abstract
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.
Keywords
Cite
@article{arxiv.2008.01513,
title = {A Learning-from-Observation Framework: One-Shot Robot Teaching for Grasp-Manipulation-Release Household Operations},
author = {Naoki Wake and Riku Arakawa and Iori Yanokura and Takuya Kiyokawa and Kazuhiro Sasabuchi and Jun Takamatsu and Katsushi Ikeuchi},
journal= {arXiv preprint arXiv:2008.01513},
year = {2021}
}
Comments
6 pages, 6 figures. Submitted to and accepted by IEEE/SICE SII 2021. Last updated October 20th, 2020