HW and embedded implementation of ML/AI algorithms for service robotic applications


The implementation of machine learning and artificial intelligence (AI) algorithms on hardware and embedded systems is essential for the development of service robotic applications. These technologies enable robots to learn from their environment, make decisions based on data, and adapt to changing conditions in real-time.

Hardware and embedded implementation of machine learning and AI algorithms require specialized expertise in computer engineering, electrical engineering, and data science. The development of these technologies has led to the creation of advanced service robotic applications, such as personal assistants, medical robots, and delivery robots.

As the demand for service robotic applications continues to grow, the implementation of machine learning and AI algorithms on hardware and embedded systems will be crucial for creating robots that can operate safely and efficiently in a wide range of environments.

An increasing effort is being put in the development of interpretable, explainable, reliable AI algorithms, in the attempt of releasing the black-box paradigm, typical of most of the present AI systems.

ERC sectors 

  • PE6_1 Computer architecture, embedded systems, operating systems
  • PE6_6 Algorithms and complexity, distributed, parallel and network algorithms, algorithmic game theory
  • PE6_7 Artificial intelligence, intelligent systems, natural language processing
  • PE6_9 Human computer interaction and interface, visualisation
  • PE6_11 Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video)
  • PE7_1 Control Engineering
  • PE7_9 Man-machine interfaces
  • PE7_10 Robotics
  • PE8_4 Computational engineering


  • Artificial intelligence
  • Machine learning
  • Inference
  • AI on the edge