A Learning-from-Observation Framework: One-Shot Robot Teaching for Grasp-Manipulation-Release Household Operations

TL;DR

This paper introduces a Learning-from-Observation framework enabling household robots to learn grasp-manipulation-release tasks from a single demonstration, incorporating geometric constraints and human postures for versatile task execution.

Contribution

The study presents a novel one-shot teaching framework that models diverse household operations and includes human postures, enhancing robot understanding and adaptability.

Findings

Framework successfully maps human demonstrations to task models.

Real robot tests demonstrate effective task execution.

Analysis shows broad coverage of 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.