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Learning from the Individuals and the Crowd in Robotics and Mobile Devices

  • Fernando E. CasadoEmail author
  • Dylan Lema
  • Roberto Iglesias
  • Carlos V. Regueiro
  • Senén Barro
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1093)

Abstract

Service robots at homes or works are expected to upload data that can be used by companies to fix the controllers and improve robot behaviours. Nevertheless, this is a delicate issue that concerns data privacy. Instead, we propose an iterative process of local learning (in the robots) and global consensus (in the cloud) that still preserves the benefits of learning from the crowd but when models instead of data are uploaded to a server. This strategy is also valid for mobile phones or other devices. In fact, in order to work with a heterogeneous community of users, we have applied our strategy in a real problem with mobile phones: walking recognition. We achieved very high performances without the need of massive amounts of centralized data.

Keywords

Semi-supervised learning Ensemble learning Continuous learning Machine learning Intelligent systems 

Notes

Acknowledgments

This research has received financial support from AEI/FEDER (EU) grant number TIN2017-90135-R, as well as the Consellería de Cultura, Educación e Ordenación Universitaria and the European Regional Development Fund (ERDF) (accreditation 2016–2019, ED431G/01 and ED431G/08 and reference competitive group ED431C 2018/29).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Fernando E. Casado
    • 1
    Email author
  • Dylan Lema
    • 1
  • Roberto Iglesias
    • 1
  • Carlos V. Regueiro
    • 2
  • Senén Barro
    • 1
  1. 1.CiTIUS (Centro Singular de Investigación en Tecnoloxías Intelixentes)Universidade de Santiago de CompostelaSantiago de CompostelaSpain
  2. 2.CITIC, Computer Architecture GroupUniversidade da CoruñaA CoruñaSpain

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