Machine Learning in Robotics


Robots act in a multimodal world and have to cope with increasingly complex tasks. These tasks have been usually dealt with using action rules and hand-crafted knowledge. This, however, limits the generalization of robotic systems to different domains and applications. Machine Learning is thus nowadays playing an important role in Robotics. Applications of machine learning techniques allow a robot to acquire novel skills or to be able to adapt itself to its environment. Examples of robot skills that can be targeted by machine learning algorithms are locomotion, grasping, manipulation, human-robot adaptive interface, robot perception and vision and linguistic abilities.


  • Machine learning

  • Data mining

  • Behavior and motion

  • Adaptation

  • Classification


  • Brígida Mónica Faria, Polytechnic of Porto (ESS - P.Porto), Portugal

  • Luis Merino, Pablo de Olavide University (UPO), Seville, Spain

  • Adrià Colomé Figueras, IRI, CSIC-UPC, Barcelona, Spain

  • Guillem Alenyà, IRI, CSIC-UPC, Barcelona, Spain

Program committee

  • Ana Lopes - University of Coimbra

  • Armando Sousa – Universidade do Porto

  • Daniel Castro Silva – Universidade do Porto

  • Fernando Caballero Benítez - University of Seville

  • João Alberto Fabro – Universidade Tecnológica Federal do Paraná

  • João Fabro - Federal University of Technology - Paraná - UTFPR – Brazil

  • João Messias – Instituto Superior Técnico

  • Luis Paulo Reis – Universidade do Porto

  • Marcelo Petry - INESC TEC's - Robotics in Industry and Intelligent Systems Center

  • Márcia Ito – Universidade de São Paulo/IBM Research Brasil

  • Noé Pérez-Higueras - Pablo de Olavide University

  • Nuno Lau – Universidade de Aveiro

  • Pedro Henriques Abreu - Universidade de Coimbra