We study algorithms that allow robots to perceive and explore the environment, by manipulating objects and interacting with humans. Motivated by research on human perception, our strategy seeks to explore active perception and multi-modal integration, whereby the robot actively explores the environment to improve perception and learning, leveraging on various sensory modalities. We perform a mix of basic and applied research in domains spanning rehabilitation, human-robot collaboration, service and industrial robotics. We also seek to extend robot autonomy with the development of software tools and methodologies for modelling and deploying robot behaviors.
Humanoid Sensing and Perception
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Ceola F., Maiettini E., Pasquale G., Meanti G., Rosasco L., Natale L.
Learn Fast, Segment Well: Fast Object Segmentation Learning on the iCub Robot
IEEE Transactions on Robotics, vol. 38, (no. 5), pp. 3154-3172