In the last decade, deep learning (DL) revolutionized artificial intelligence in almost all its fields of application. Indeed, it allowed to reach impressive results in both perception (computer vision, natural language processing) and decision making (reinforcement learning, navigation). This drastic change was aided by a huge boost in computing capabilities, and by the development of components specifically dedicated to massive data processing.

However, robotic systems usually lack the computational power typically dedicated to deep learning algorithms due to cost and dimensions. Nevertheless, control applications (like autonomous navigation, for example) must give particular attention to latency and power consumption, as the slightest delay could cause severe damages. Due to these challenges, model optimization and edge AI have become of enormous interest in the last few years, as embedding such powerful computing closer to sensors and actuators is the key for the future of intelligent robotics.

For this reason, my research focuses on the development of deep learning models to obtain intelligent sensors for robot perception, control and decision making. Particular attention is given to the constraints imposed by the application field in terms of latency, power consumption and weight.