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Elena Merlo
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About

Elena Merlo is a Postdoctoral Researcher at the Human-Robot Interfaces and Interaction Laboratory of the Istituto Italiano di Tecnologia. She received the BD in Biomedical Engineering (2019) and the MD in Robotics Engineering (2021), both with full mark, and completed her Ph.D. cum laude in Biomedical Engineering and Robotics from Università degli Studi di Genova (2025). Her research focuses on intuitive robot programming, combining task planning and role allocation with human-interpretable plan representations, and leveraging LLMs to enable natural-language inspection, refinement, and adaptation of robot behaviors.

Education

Title: BS in Biomedical Engineering
Institute: Università degli Studi di Genova
Location: Genova
Country: Italia
From: 2016 To: 2019

Title: MS in Robotics Engineering
Institute: Università degli Studi di Genova
Location: Genova
Country: Italia
From: 2019 To: 2021

Title: PhD in Bioengineering and Robotics
Institute: Istituto Italiano di Tecnologia
Location: Genova
Country: Italia
From: 2022 To: 2025

All Publications
2026
Lagomarsino M., Merlo E., Pupa A., Birr T., Krebs F., Secchi C., Asfour T., Ajoudani A.
Intuitive Programming, Adaptive Task Planning, and Dynamic Role Allocation in Human-Robot Collaboration
Annual Review of Control, Robotics, and Autonomous Systems, vol. 9
2025
Merlo E., Lagomarsino M., Ajoudani A.
A Human-in-The-Loop Approach to Robot Action Replanning Through LLM Common-Sense Reasoning
IEEE Robotics and Automation Letters, vol. 10, (no. 10), pp. 10767-10774
2025
Merlo E., Lagomarsino M., Lamon E., Ajoudani A.
Exploiting Information Theory for Intuitive Robot Programming of Manual Activities
IEEE Transactions on Robotics
2025
Merlo E., Lagomarsino M., Ajoudani A.
Information-Theoretic Detection of Bimanual Interactions for Dual-Arm Robot Plan Generation
IEEE Robotics and Automation Letters, vol. 10, (no. 5), pp. 4532-4539
Article Journal
2023
Merlo E., Lamon E., Fusaro F., Lorenzini M., Carfi A., Mastrogiovanni F., Ajoudani A.
An ergonomic role allocation framework for dynamic human–robot collaborative tasks
Journal of Manufacturing Systems, vol. 67, pp. 111-121