Design and implementation of an object learning system for service robots by using random forest, convolutional neural network, and gated recurrent neural network

Chih Yin Liu, Cheng Hui Li, Tzuu Hseng S. Li, Cheng Ying Hsieh, Ching Wen Cheng, Chih Yen Chen, Yu Ting Su

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Inspired by the self-exploring learning approach, this paper proposes an object-learning system in which a robot interacts with objects to obtain their features and construct object concepts. The system consists of three kinds of features: Interaction features, visual features, and intrinsic features. When the robot interacts with an object, it observes the changes in the object to obtain its interaction features. At the same time, the robot learns the visual features of the object. The intrinsic features are the properties of the object. Models of the relationships among the three kinds of features are constructed through an Artificial Bee Colony based Random Forest algorithm and a Convolutional Neural Network. The established models help the robot to predict the properties of new objects and to make decisions. Two experiments are constructed in this paper: The service-providing task and the stacking task. In the former, the robot decides on an appropriate object, using the object concept models, to accomplish an appointed task. In the second experiment, the robot uses a Gated Recurrent Neural Network to learn the stacking sequence of various objects. All the experimental results demonstrate that the robot can build object concept models by interacting with objects, and can utilize these models to accomplish various tasks.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages933-940
Number of pages8
ISBN (Electronic)9781728145693
DOIs
Publication statusPublished - 2019 Oct
Event2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 - Bari, Italy
Duration: 2019 Oct 62019 Oct 9

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume2019-October
ISSN (Print)1062-922X

Conference

Conference2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
CountryItaly
CityBari
Period19-10-0619-10-09

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

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    Liu, C. Y., Li, C. H., Li, T. H. S., Hsieh, C. Y., Cheng, C. W., Chen, C. Y., & Su, Y. T. (2019). Design and implementation of an object learning system for service robots by using random forest, convolutional neural network, and gated recurrent neural network. In 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 (pp. 933-940). [8914311] (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics; Vol. 2019-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2019.8914311