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.

keywords: Semi-supervised learning, Ensemble learning, Continuous learning, Machine learning, Intelligent systems