Learning from the Individuals and the Crowd in Robotics and Mobile Devices
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.
KeywordsSemi-supervised learning Ensemble learning Continuous learning Machine learning Intelligent systems
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).
- 1.Vincent, J.: The mobile phone: an emotionalised social robot. In: Social Robots from a Human Perspective, pp. 105–115. Springer (2015)Google Scholar
- 2.Nakkiran, P., Alvarez, R., Prabhavalkar, R., Parada, C.: Compressing deep neural networks using a rank-constrained topology. In: Proceedings of the 16th Annual Conference of the International Speech Communication Association (2015)Google Scholar
- 3.Vasilyev, A.: CNN optimizations for embedded systems and FFT. Standford University Report (2015)Google Scholar
- 4.Lane, N.D., Georgiev, P.: Can deep learning revolutionize mobile sensing? In: Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications, pp. 117–122. ACM (2015)Google Scholar