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
Laboratories
We are among the groups that have contributed to the development of the iCub robot. Our laboratories are equipped with several humanoid robots (two iCub and an ergoCub humanoid robots), including computing equipment, HPC servers, a small machine shop, mechanical and electronic design facilities.
The team is composed of computer scientists and engineers with competences spanning computer vision, signal processing, machine learning and software engineering.
More details
To know more about our group you can follow the links on the top menu
- Research: a list of research topics and research projects
- Software: a list of open source software and datasets
- Media: gallery of pictures and videos demonstrating results of our research
- Publications
Interested in joining us? Have a look at our openings at the right side of this page.
People
Principal Investigator
Humanoid Sensing and Perception
Selected Publications
2023
Bottarel F., Altobelli A., Pattacini U., Natale L.
GRASPA-fying the Panda: Easily Deployable, Fully Reproducible Benchmarking of Grasp Planning Algorithms
IEEE Robotics and Automation Magazine
2022
Colledanchise M., Natale L.
Handling Concurrency in Behavior Trees
IEEE Transactions on Robotics, vol. 38, (no. 4), pp. 2557-2576
2022
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