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  • 40 under 40 by Fortune Italia.  Selected to be on the cover of the July/August 2022 Fortune Italia magazine.



    Selected as one of the finalists for the best paper award at the IEEE International Conference on Development and Learning 2021 for the paper Garello, L., Lastrico, L., Rea, F., Mastrogiovanni, F., Noceti, N., & Sciutti, A. (2021). Property-Aware Robot Object Manipulation: a Generative Approach.


    Alessandra Sciutti: International prize Tecnovisionarie 2021, promoted by Women&Tech dedicated to excellent women in AI, for the category AI and Robotics


    Radoslaw Niewiadomski: Honorable mention at the 26th International Conference on Intelligent User Interfaces (IUI 2021)for the paper "Multimodal Emotion Recognition of Hand-Object Interaction"


    Alessandra Sciutti: Selected to be included in the database of profiles of excellent female researchers from all disciplines since 24.06.2021


    Alessandra Sciutti: Selected as an ELLIS Scholar in the ELLIS (European Lab for Learning and Intelligent Systems) Unit od Genoa.

  • International Prize

    Alessandra Sciutti: Prize promoted by Women&Tech dedicated to excellent women in AI and Robotics


  • Best contribution

    Giulia Belgiovine: Best contribution in AI and Robotics Award at the Second Italian Conference in Robotics and Intelligent machines (I-RIM) for the work "Towards Effective Robot Tutoring for Skills Acquisition"


    Giulia Belgiovine: Best paper award at the International Conference on Social Robotics 2020 for the wHiSPER paper "Toward Effective Robot Tutoring in Motor Skill Learning"

  • Honorable mention

    Carlo Mazzola: Honorable mention at the ACM/IEEE Human Robot Interaction Conference 2020 for the paper "Interacting with a Social Robot Affects Visual Perception od Space"


    Selected as one of the finalists for the best paper award at the International Workshop on Human Friendly Robotics (HFR) with the paper Lastrico L., Carfì A., Vignolo A., Sciutti A., Mastrogiovanni F., Rea F. 2020 ‘Careful with That! Observation of Human Movements to Estimate Objects Properties’


    Most popular presentation runner-up of the conference track  at the Tenth Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob 2020) for the work Barros P., Tanevska A., Cruz F., Sciutti A. 2020 'Moody Learners - Explaining Competitive Behaviour of Reinforcement Learning Agents'.



    Alessandra Sciutti: Selected as one of the 15 most influential women in the digital world in Italy by Digitalic


    Alessandra Sciutti: selected one of the finalists for the best paper award at the Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EPIROB) with the paper Sciutti A., Sandini G. 2019, ‘The role of object motion in visuo-haptic exploration during development’, Oslo, Norway, August 19-22, 2019

"Interacting with a Social Robot Affects Visual Perception of Space", the paper presented at HRI 2020 International Conference on Human-Robot Interaction, has been awarded with a Honorable Mention

Human partners are very effective at coordinating in space and time. Such ability is particular remarkable considering that visual perception of space is a complex inferential process, which is affected by individual prior experience (e.g. the history of previous stimuli). As a result, two partners might perceive differently the same stimulus. Yet, they find a way to align their perception, as demonstrated by the high degree of coordination observed in sports or even in everyday gestures as shaking hands. Robots would need a similar ability to align with their partner's perception. However, to date there is no knowledge of how the inferential mechanism supporting visual perception operates during social interaction. In the current work, we use a humanoid robot to address this question. We replicate a standard protocol for the quantification of perceptual inference in a HRI setting. Participants estimated the length of a set of segments presented by the humanoid robot iCub. The robot behaved in one condition as a mechanical arm driven by a computer and in another condition as an interactive, social partner. Even if the stimuli presented were the same in the two conditions, length perception was different when the robot was judged as an interactive agent rather than a mechanical tool. When playing with the social robot, participants relied significantly less on stimulus history. This result suggests that the brain changes optimization strategies during interaction and lay the foundations to design humanaware robot visual perception.