Ontology Learning of New Concepts combining Textural Knowledge and Visual Analysis for Service Robot Applications

Gómez, L. Villamar; Miura, Jun

doi:10.1109/isr50024.2021.9419551

Cited by 1 publication

(3 citation statements)

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“…The first experiment consisted of the robot learning only one object concept, and it did not include interaction with the user. This experiment is a continuation of the experiments performed in [7]. This experiment aimed to show the importance of visual information to assist in selecting the semantic relation corresponding to the new object concept.…”

Section: B Concept Learning Experimentsmentioning

confidence: 97%

“…Our previous work [7] showed an early version of this system, which used a basic visual analysis. It demonstrated the importance of selecting the proper meaning of a new concept during the learning process.…”

Section: Introductionmentioning

confidence: 99%

“…FIGURE 1. Concept learning scenario[7]. The robot receives the name of a new object along with its visual image.…”

mentioning

confidence: 99%

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Ontology Learning of New Concepts Combining Textural Knowledge, Visual Analysis, and User Interaction

Gómez

Miura

2021

IEEE Access

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The continuous development of new technology has been contributing to making service robots an upcoming reality. Robots need to adapt to changing environments when interacting with people is fundamental. Hence, service robots at home need learning capabilities to acquire new knowledge and merge it with their own. In this system, a robot is provided with an essential feature to adapt inside a home environment. We focus on the learning of new ontological concepts oriented to service robot applications. We propose combining textural knowledge, visual analysis and user interaction to determine the correct placement of the new concept in the ontology structure. We aim to make the robot able to extend its ontological knowledge as needed. We conducted a set of experiments to show the applicability of the presented method and the advantage of having the objects conceptualized in ontological knowledge. The experiments consisted of two parts: concept learning experiments and experiments with an integrated robot system. In the concept learning experiments, the robot had to conceptualize a set of new objects in its ontological knowledge. While in the integrated robot system experiments, the robot was asked to search and find the new objects learned.INDEX TERMS Concept learning, ontology learning, robot learning, human-robot interaction.

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Section: B Concept Learning Experimentsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%