Arantxa Renteria

TL;DR: Some of the lessons learned by adopting an incremental and progressive approach driven by the feedback received from its own activities and from the user are summarized and promising directions for future work are outlined.

TL;DR: This paper briefly illustrates how a unique combination of methods from cognitive robotics, agent control systems, wireless sensor networks and machine learning are being extended, integrated, and applied to AAL applications.

TL;DR: This paper demonstrates how the integrated and self-organising cognitive solutions implemented within the EU project RUBICON (Robotic UBIquitous Cognitive Network) can reduce the need of costly pre-programming and maintenance of robotic ecologies.

TL;DR: A data benchmark for the assessment of human activity recognition solutions, collected as part of the EU FP7 RUBICON project, and available to the scientific community is presented.

TL;DR: The architecture for an adaptive robotic ecology, named RUBICON, consisting of a network of sensors,effectors and mobile robots is illustrated, claiming that endowing these systems with learning capabilities can greatly increase their viability and acceptability, by simplifying design, customization and adaptation to changing user needs.