A tutorial about “Signal Processing for Self awareness in Autonomous systems” will take place during EUSIPCO 2018 in Rome, Italy. Presenters are Carlo Regazzoni and Lucio Marcenaro from the University of Genova, Italy.


The tutorial aims at providing an overview of new insights in introducing dynamic self-awareness structured models and inference in artificial autonomous systems. Over the last decade, researchers have been proposing and investigating computing systems with advanced levels of autonomy in order to manage the ever-increasing requirements in complexity. Cognitive Dynamic Systems (CDS) are one particular approach to tackle these challenges. CDS aim at building up rules of behavior over time through learning from continuous experiential interactions with the environment. By exploiting these rules, CDS can deal with environmental dynamics and uncertainties and have therefore leveraged the automation of tasks with complex perception-action cycles including surveillance, cognitive radio, traffic control and robot mediated industrial and domestic applications. However, autonomous systems and in particular CDS lack in adaptability to internal and external non-stationary conditions. Many real-world systems frequently experience non-stationary conditions (i.e., unknown situations) due to uncertain interactions with the environment (incl. human agents) and users, failures or structural changes.

This tutorial describes recent advancements in last generation autonomous systems where self-awareness methods can be introduced that are based on fusion of multimodal signals into dynamic behavioral models. Self-awareness is a broad concept which describes the property of a system, which has knowledge of “itself”, based on its own senses and internal models. This knowledge may take different forms, is based on perceptions of both internal and external phenomena and is essential for being able to anticipate and adapt to unknown situations. Self-awareness (in a computational context) is founded on advanced methods and algorithms from different disciplines including signal processing, machine learning, control engineering and decision making.

Self-awareness models can be learned from data about experiences where a teacher has shown an entity how to perform a task, as human do. Learned self-awareness models can be used for different purposes like

  • predicting self and external situation evolution,
  • detecting non-stationary conditions
  • selecting the best way to adapt agent behavior to current conditions based on the set of learned behaviors.

The tutorial comprehensively addresses self-awareness in autonomous systems along with multiple fundamental and practical dimensions.

Read more here

Tutorial at EUSIPCO2018