Below follow a summary of the recent results of my research grouped in different themes (Tactile sensing, Vision and Software Engineering). For a complete list of papers see my publications list (Publications).
AI based control of prosthetic hand
I have worked on a computer vision-based system that uses AI methods for controlling the degrees of freedom of a prosthetic arm in a shared control paradigm. In various contributions we explored methods that adapt Computer Vision techniques, robot grasping and, more recently, generative AI method via imitation learning :
References
Alessi, C., Vasile, F., Ceola, F., Pasquale, G., Boccardo, N., and Natale, L., HannesImitation: Grasping with the Hannes Prosthetic Hand via Imitation Learning, in IEEE/RSJ International Conference on Intelligent Robots and Systems, Hangzhou, China, 2025. [project page][preprint][bibtex]
Vasile, F., Maiettini, E., Pasquale, G., Boccardo, N., and Natale, L., Continuous Wrist Control on the Hannes Prosthesis: A Vision-Based Shared Autonomy Framework, in IEEE-RAS International Conference on Robotics and Automation, Atlanta, GA, USA, 2025. [project page][doi][bibtex]
Stracquadanio, G., Vasile, F., Maiettini, E., Boccardo, N., and Natale, L., Bring Your Own Grasp Generator: Leveraging Robot Grasp Generation for Prosthetic Grasping, in IEEE-RAS International Conference on Robotics and Automation, Atlanta, GA, USA, 2025. [project page][doi][bibtex]
Vasile, F., Maiettini, E., Pasquale, G., Florio, A., Boccardo, N., and Natale, L., Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis, in IEEE/RSJ International Conference on Intelligent Robots and Systems, Japan, 2022. [preprint][doi][bibtex]
Robot Learning for Object manipulation and grasping
The iCub is equipped with a system of about 4500 tactile sensors distributed on the torso, arms and legs. I have worked in particular on the control of interaction forces on the whole-body and on the design of the sensorized fingertips. At the moment my group is studying algorithms for object grasping and manipulation using visual and tactile feedback.
References
Rosasco, A., Ceola, F., Pasquale, G., and Natale, L., KDPE: A Kernel Density Estimation Strategy for Diffusion Policy Trajectory Selection, in Conference on Robot Learning, Seoul, Korea, 2025. [project page][doi][preprint][bibtex]
Puang, E. Y., Ceola, F., Pasquale, G., and Natale, L., PCHands: PCA-based Hand Pose Synergy Representation on Manipulators with N-DoF, in IEEE-RAS Humanoids, Seoul, Korea, 2025. [project page][doi][preprint][bibtex]
Caddeo, G. M., Marcani, A., Alfano, P. D., Rosasco, L., and Natale, L., Sim2Real Bilevel Adaptation for Object Surface Classification using Vision-Based Tactile Sensors, in IEEE-RAS International Conference on Robotics and Automation, Yokohama, Japan, 2024. [preprint][doi][bibtex]
Caddeo, G., Piga, N., A., Bottarel, F., and Natale, L. Collision-aware In-hand 6D Object Pose Estimation using Multiple Vision-based Tactile Sensors, in Proc. IEEE International Conference on Robotics and Automation London, UK, 2023. [preprint][bibtex]
Bottarel, F., Altobelli, A., Pattacini, U., and Natale, L., GRASPA-fying the Panda: Easily Deployable, Fully Reproducible Benchmarking of Grasp Planning Algorithms, IEEE Robotics and Automation Magazine, 2023. [fulltext][bibtex]
Bottarel, F., Vezzani, G., Pattacini, U. and Natale, L., GRASPA 1.0: GRASPA is a Robot Arm graSping Performance benchmArk, IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 836-843, 2020. [online text] [preprint][bibtex]
Vezzani, G., Pattacini, U., Pasquale, G., and Natale, L., Improving Superquadric Modeling and Grasping with Prior on Object Shapes, in Proc. IEEE-RAS International Conference on Robotics and Automation, Brisbane, Australia, 2018, pp. 6875-6882.
[pdf] [bibtex]
Fantacci, C., Vezzani, G., Pattacini, U., Tikhanoff, V., and Natale, L., Markerless visual servoing on unknown objects for humanoid robot platforms, in Proc. IEEE-RAS International Conference on Robotics and Automation, Brisbane, Australia, 2018, pp. 3099-3106.[pdf] [bibtex]
Regoli, M., Pattacini, U., Metta, G., and Natale, L., Hierarchical Grasp Controller Using Tactile Feedback, in IEEE-RAS International Conference on Humanoid Robots, Cancun, Mexico, 2016.[pdf] [bibtex]
Jamali, N., Maggiali, M., Giovannini, F., Metta, G., and Natale, L., A New Design of a Fingertip for the iCub Hand, in Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, Hamburg, Germany, 2015, pp. 1799-1805. [pdf] [bibtex]
Maiolino, P.,
Maggiali, M., Cannata, G., Metta, G., Natale, L., A Flexible
and Robust Large Scale Capacitive Tactile System for Robots, IEEE Sensors
Journal, vol. 13, no. 10, pp. 3910-3917, 2013. [pdf] [bibtex]
Del Prete, A.,
Denei, S., Natale, L., Mastrogiovanni, F., Nori, F., Cannata, G.,
Metta, G., Skin Spatial Calibration Using Force/Torque
Measurements, IEEE/RSJ International Conference on
Intelligent Robots and Systems, San Francisco, California, September
25-30, 2011. [pdf]
Schmitz
A., Maiolino P., Maggiali M., Natale L., Cannata G., Metta
G., Methods and Technologies for the Implementation of Large
Scale Robot Tactile Sensors, IEEE Transactions on Robotics,
Volume 27, Issue 3, pp. 389-400, 2011. [pdf] [bibtex]
Robot Vision
I am interested in the study of algorithms for visual perception of the scene, online learing with weak supervision, object detection and segmentation.
References
Galliena, T., Apicella, T., Rosa, S., Morerio, P., Del Bue, A., and Natale, L., Embodied Image Captioning: Self-supervised Learning Agents for Spatially Coherent Image Descriptions, in IEEE/CVF International Conference on Computer Vision (ICCV), Honolulu, Hawaii, 2025. [project page][preprint][bibtex]
Scarpellini, G., Stefano, R., Moreiro, P., Natale, L., and Del Bue, A., Look around and learn: improving object detection by exploration, in European Conference on Computer Vision, 2024. [project page][doi][bibtex]
Ceola, F., Maiettini, E., Pasquale, G., Meanti, G., Rosasco, L., and Natale, L., Learn Fast, Segment Well: Fast Object Segmentation Learning on the iCub Robot, IEEE Transactions on robotics, 2022. [full text][bibtex]
Maiettini, E., Pasquale, G., Rosasco, L., and Natale, L., On-line Object Detection: a Robotics Challenge, Autonomous Robots, vol. 44, no. 5, pp. 739–757, 2020. [online text] [preprint][bibtex]
Maiettini, E., Pasquale, G., Rosasco, L., and Natale, L., Speeding-up Object Detection Training for Robotics with FALKON, in Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, Madrid, Spain 2018, pp. 5770-5776.[pdf] [bibtex]
Pasquale, G., Ciliberto, C., Odone, F., Rosasco, L., and Natale, L., Are we done with object recognition? The iCub robot’s perspective, Robotics and Autonomous Systems, vol. 112, pp. 260-281, 2019.
[online full text][preprint] [bibtex]
Abuhashim, T., and Natale, L., Robustness in View-Graph SLAM , in 19th International Conference on Information Fusion, Heidelberg, Germany, 2016, pp. 942-949. [pdf] [bibtex]
Roncone, A., Pattacini, U., Metta, G., and Natale, L., Cartesian 6-DoF gaze controller for humanoid robots, in Robotics: Science and Systems, Ann Arbor, Michigan, USA, 2016. [pdf] [bibtex]
Pasquale, G., Ciliberto, C., Odone, F., Rosasco, L., and Natale, L., Teaching iCub to recognize objects using deep Convolutional Neural Networks, in Proc. 4th Workshop on Machine Learning for Interactive Systems, 2015.
Mar, T.,
Tikhanoff, V., Metta, G., and Natale, L.,
Self-supervised learning of grasp dependent tool affordances on the
iCub Humanoid robot, in Proc. IEEE International Conference
on Robotics and
Automation, Seattle, Washington, 2015.
Ciliberto, C.,
Smeraldi, F., Natale, L., Metta, G., Online Multiple Instance
Learning Applied to Hand Detection in a Humanoid Robot,
IEEE/RSJ International Conference on Intelligent Robots and Systems,
San Francisco, California, September 25-30, 2011.
Fanello, S. R.,
Ciliberto C., Natale, L., Metta, G., Weakly Supervised
Strategies for Natural Object Recognition in Robotics, in
Proc. IEEE International Conference on Robotics and Automation,
Karlsruhe, Germany, 2013, pp. 4223-4229.
Software engineering
Robotics is a system science. For this reason a large body of work has been devoted to the development and study of software archictures and software engineering techniques specifically tailored to robotics systems.
During the development of the iCub robot I led the engineering of the iCub software. We have developed YARP (www.yarp.it) an open source middleware that supports concurrent execution of components on a cluster of computers.
Because communication is a typical bottleneck in robotics systems we have extended YARP to provide support for channel prioritization and QoS.
With my group I have also studied techniques for coordinating software components, supporting reactive bahaviors and behavior reusability.
We studied behavior based architectures and behavior trees. More recently we have been looking at formal methods for static and runtime verification of robot behaviors.
References
Bernagozzi, S., Faraci, S., Ghiorzi, E., Pedemonte, K., Natale, L., and Tacchella, A., Code Generation and Monitoring for Deliberation Components in Autonomous Robots, in IEEE/RSJ International Conference on Intelligent Robots and Systems, Hangzhou, China, 2025. [pdf][bibtex]
Bernagozzi, S., Faraci, S., Ghiorzi, E., Pedemonte, K., Ferrando, A., Natale, L., and Tacchella, A., Model-based Verification and Monitoring for Safe and Responsive Reactive Robots, in IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, 2025. [doi][pdf][bibtex]
Ghiorzi, E., Colledanchise, M., Piquet, G., Tacchella, A., and Natale, L., Learning linear temporal properties for autonomous robotic systems, IEEE Robotics and Automation Letters, 2023. [fulltext][bibtex]
Colledanchise, M., and Natale, L., Handling Concurrency in Behavior Trees, IEEE Transactions on robotics, , vol. 38, no. 4, pp. 2557-2576, 2022. [preprint][bibtex]
Colledanchise, M., and Natale, L., Analysis and Exploitation of Synchronized Parallel Executions in Behavior Trees, in Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, Macau, China, 2019, pp. 6399-6406. [pdf] [bibtex]
Natale, L., Paikan, A., Randazzo, M., Domenichelli, D.E.,The iCub Software Architecture: Evolution and Lessons Learned, Frontiers in Robotics and AI, vol. 3, no.24, 2016, doi: 10.3389/frobt.2016.00024. [pdf] [bibtex]
Paikan, A., Pattacini, U., Domenichelli, D., Randazzo, M., Metta, G., and Natale, L.,A Best-Effort Approach for Run-Time Channel Prioritization in Real-Time Robotic Application, in Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, Hamburg, Germany, 2015, pp. 498-503. [pdf] [bibtex]
Paikan, A., Traversaro, S., Nori, F., and Natale, L.,Generic
Testing Framework for Test Driven Development of Robotic Systems, in Proc. Modelling and Simulation for Autonomous Systems Workshop, Prague, Czech Republic, 2015, pp. 216-225. [pdf] [bibtex]
Paikan, A.,
Domenichelli, D., and Natale, L., Communication channel
prioritization in a publish-subscribe architecture, in Proc.
Software
Engineering and Architectures for Realtime Interactive Systems
Workshop, Arles, France, 2015. [pdf]
Paikan,
A., Tikhanoff, V., Metta, G., and Natale, L.,
Enhancing software module reusability
using port plug-ins: an
experiment with the iCub robot, in Proc. IEEE/RSJ
International Conference
on Intelligent Robots and Systems, Chicago, Illinois, 2014. [pdf]
Paikan,
A., Fitzpatrick, P., Metta, G., and Natale, L.,
Data Flow Port's Monitoring and Arbitration,
Journal of Software
Engineering for Robotics, vol. 5, no. 1, pp. 80-88, 2014. [pdf]
Fitzpatrick, P., Metta, G., and Natale, L., Towards Long-Lived Robot Genes,
Robotics and Autonomous Systems, Volume 56, Issue 1, pp. 29-45,
Elsevier 2008. [pdf]