Multi-Modal Robot Apprenticeship: Imitation Learning Using Linearly Decayed DMP+ in a Human-Robot Dialogue System
| Autor: | Yan Wu, Keng Peng Tee, Ruohan Wang, Rafael E. Banchs, Luis Fernando D'Haro |
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| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Modality (human–computer interaction) Computer science 02 engineering and technology Ontology (information science) Robot learning Human–robot interaction Task (computing) 020901 industrial engineering & automation Human–computer interaction 0202 electrical engineering electronic engineering information engineering Task analysis Robot 020201 artificial intelligence & image processing |
| Zdroj: | IROS |
| DOI: | 10.1109/iros.2018.8593634 |
| Popis: | 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 real-life 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. |
| Databáze: | OpenAIRE |
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