Alessandro Umbrico

About

Alessandro Umbrico is a prominent AI and robotics researcher whose work sits at the intersection of human-robot collaboration, task planning, and assistive technologies. Based primarily at the National Research Council of Italy, Umbrico has made foundational contributions to the development of intelligent systems that enable seamless cooperation between humans and robots in both industrial and social contexts. His most cited work, "Motion Planning and Scheduling for Human and Industrial-Robot Collaboration" (2017, 110 citations), established key frameworks for coordinating robotic and human actions in shared workspaces. Alongside complementary research on symbiotic collaboration and AI-driven task planning, this body of work has significantly shaped how modern manufacturing systems handle human-robot teaming. Umbrico also developed PLATINUm, an influential planning and execution framework that underpins much of his subsequent research. Beyond industrial settings, Umbrico has devoted considerable effort to socially assistive robotics and Active Assisted Living, with a notable retrospective spanning nearly 18 years of AI solutions designed to support older adults' independence. His holistic approach to behavior adaptation in assistive robots (2020, 71 citations) reflects a deep commitment to human-centered design. With over 470 combined citations across his top works, Umbrico's research continues to bridge theoretical AI planning with real-world robotic applications.

Research Focus

Key Achievements

Top Papers

1. 1
   Motion planning and scheduling for human and industrial-robot collaboration

   110 citations · 2017
2. 2
   A Holistic Approach to Behavior Adaptation for Socially Assistive Robots

   71 citations · 2020
3. 3
   AI and robotics to help older adults: Revisiting projects in search of lessons learned

   49 citations · 2021
4. 4
   Towards a planning-based framework for symbiotic human-robot collaboration

   44 citations · 2016
5. 5
   PLATINUm: A New Framework for Planning and Acting

   41 citations · 2017
6. 6
   Fostering Robust Human-Robot Collaboration through AI Task Planning

   36 citations · 2018
7. 7
   An Ontology for Human-Robot Collaboration

   36 citations · 2020
8. 8
   Knowledge-based adaptive agents for manufacturing domains

   34 citations · 2018
9. 9
   A Layered Control Approach to Human-Aware Task and Motion Planning for Human-Robot Collaboration

   28 citations · 2020
10. 10
    Optimal Task and Motion Planning and Execution for Multiagent Systems in Dynamic Environments

    27 citations · 2023

Key Collaborators

Contact & Links