Carlotta Orsenigo

About

Carlotta Orsenigo is a researcher at the forefront of intelligent human-robot interaction (HRI) and industrial AI, with a primary focus on vision-based hand analysis and acoustic scene understanding. Her work addresses two critical challenges in collaborative robotics: enabling natural communication through hand gestures and ensuring safety through sound source localization. Orsenigo’s major contributions include pioneering deep learning models for hand detection, segmentation, and gesture recognition, which are essential for intuitive HRI. Notably, her 2025 review on vision-based hand analysis has already garnered 5 citations, highlighting its significance as a comprehensive resource in the field. She has also advanced industrial safety with her 2024 work on ConvLSTM-based sound source localization in manufacturing environments (7 citations), demonstrating how AI can detect active sound sources to prevent accidents. Her 2025 study on testing hand segmentation under in-distribution and out-of-distribution data (4 citations) further showcases her commitment to robust, real-world applications. Orsenigo’s research, with a growing citation impact, is shaping the future of safe, seamless human-robot collaboration in dynamic settings.

Research Focus

Key Achievements

Top Papers

1. 1
   ConvLSTM-based Sound Source Localization in a manufacturing workplace

   7 citations · 2024
2. 2
   A review on deep learning for vision-based hand detection, hand segmentation and hand gesture recognition in human–robot interaction

   5 citations · 2025
3. 3
   Testing human-hand segmentation on in-distribution and out-of-distribution data in human–robot interactions using a deep ensemble model

   4 citations · 2025
4. 4
   A Conceptual Framework for Localization of Active Sound Sources in Manufacturing Environment Based on Artificial Intelligence

   3 citations · 2023

Key Collaborators

Contact & Links