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Robot Vision

Application of Deep Learning methods in robotics is difficult, due to their data and computational requirements. We study methods for visual perception, such as object recognition, detection and segmentation, that can be trained on-line, methods for object pose tracking that are robust to occlusions and can efficiently track moving objects. To support on-line learning, we devise strategies that allow robots to leverage on the interaction with humans and the environment to acquire training examples autonomously.

  • 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, 2022, IEEE Transactions on robotics.
  • Piga, N., Onyshchuk, Y., Pasquale, G., Pattacini, U., and Natale, L., ROFT: Real-time Optical Flow-aided 6D Object Pose and Velocity Tracking, IEEE Robotics and Automation Letters, vol. 7, no. 1, pp. 159-166, 2022. 
  • Maiettini, E., Pasquale, G., Rosasco, L., and Natale, L., On-line Object Detection: a Robotics Challenge, 2020, Autonomous Robots, vol. 44, no. 5, pp. 739–757,
  • 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.

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Multimodal manipulation

Grasping and manipulation allows robots to perform useful tasks but also explore objects to acquire data for learning. We seek to improve grasping capabilities of humanoid robots using multi-modal information (i.e., vision, touch and proprioception). In past research we have proposed novel methods for object modelling and grasping using superquadrics, and hand detection for precise visual control. Recent contributions include methods for shape completion, in-hand object pose estimation and tracking with tactile feedback. We also proposed methodologies for benchmarking grasping algorithms.

  • 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.
  • Rosasco, A., Berti, S., Bottarel, F., Colledanchise, M., and Natale, L., Towards Confidence-guided Shape Completion for Robotic Applications, in Proc. IEEE-RAS International Conference on Humanoid Robots, 2022.
  • 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. 
  • 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. 
  • 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.

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Software engineering

In robotics, a large body of work has been devoted to the development and study of software architectures and software engineering techniques specifically tailored to robotics systems. We develop YARP (www.yarp.it) an open source middleware that supports concurrent execution of components on a cluster of computers. In recent work we have been looking at BehaviorTrees as a method for designing and implementing reactive and modular robot behaviors, and formal methods for static and runtime verification of robot behaviors.

  • 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. 
  • Colledanchise, M., and Natale, L., Handling Concurrency in Behavior Trees, IEEE Transactions on robotics, , vol. 38, no. 4, pp. 2557-2576, 2022. 
  • 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. 
  • 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.

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