Multi-modal Robot Apprenticeship: Imitation Learning using Linearly Decayed DMP+ in a Human-Robot Dialogue System

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Multi-modal Robot Apprenticeship: Imitation Learning using Linearly Decayed DMP+ in a Human-Robot Dialogue System
Title:
Multi-modal Robot Apprenticeship: Imitation Learning using Linearly Decayed DMP+ in a Human-Robot Dialogue System
Journal Title:
IROS2018
DOI:
Publication URL:
Keywords:
Publication Date:
05 October 2018
Citation:
Abstract:
Robot learning by demonstration gives robots the ability to learn tasks which they have not been programmed to do before. The paradigm allows robots to work in a greater range of real-world applications in our daily life. However, this paradigm has traditionally been applied to learn tasks from a single demonstration modality. This restricts the approach to be scaled to learn and execute a series of tasks in a reallife environment. In this paper, we propose a multi-modal learning approach using DMP+ with linear decay integrated in a dialogue system with speech and ontology for the robot to learn seamlessly through natural interaction modalities (like an apprentice) while learning or re-learning is done on the fly to allow partial updates to a learned task to reduce potential user fatigue and operational downtime in teaching. The performance of new DMP+ with linear decay system is statistically benchmarked against state-of-the-art DMP implementations. A gluing demonstration is also conducted to show how the system provides seamless learning of multiple tasks in a flexible manufacturing set-up.
License type:
PublisherCopyrights
Funding Info:
Description:
(c) 2018 IEEE.
ISBN:
978-1-5386-8093-3
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