Mohammed Diab

About

Mohammed Diab is a robotics researcher whose work sits at the intersection of knowledge representation, autonomous manipulation, and human-robot interaction. He is best known for his contributions to ontology-based approaches in robotics, most notably his highly cited 2019 review comparing ontological frameworks for robot autonomy, which has accumulated over 120 citations and serves as a foundational reference in the field. His research focuses on enabling robots to reason about their environments, plan complex manipulation tasks, and adapt autonomously to uncertainty — challenges he addressed through frameworks such as PMK (62 citations) and SkillMaN, which integrate perception, semantic knowledge, and geometric reasoning for manipulation. Diab has also contributed to international standardization efforts, participating in the development of the IEEE Standard for Autonomous Robotics Ontology, reflecting his influence beyond academic publishing. His more recent work explores trust in human-robot interaction, including methods for modeling and transferring trust-related knowledge during assistive tasks — a growing priority as robots enter everyday environments. Notably, his research breadth extends into psychometric methodology, evidenced by a Delphi-based scale development study. Across these domains, Diab's cumulative impact underscores his role as a versatile and forward-thinking contributor to intelligent, human-centered robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   A review and comparison of ontology-based approaches to robot autonomy

   120 citations · 2019
2. 2
   PMK—A Knowledge Processing Framework for Autonomous Robotics Perception and Manipulation

   62 citations · 2019
3. 3
   Preliminary psychometric scale development using the mixed methods Delphi technique

   35 citations · 2022
4. 4
   SkillMaN — A skill-based robotic manipulation framework based on perception and reasoning

   23 citations · 2020
5. 5
   Contingent Task and Motion Planning under Uncertainty for Human–Robot Interactions

   15 citations · 2020
6. 6
   Automating Adaptive Execution Behaviors for Robot Manipulation

   14 citations · 2022
7. 7
   An Ontology for Failure Interpretation in Automated Planning and Execution

   13 citations · 2019
8. 8
   A framework for trust-related knowledge transfer in human–robot interaction

   12 citations · 2024
9. 9
   IEEE Standard for Autonomous Robotics Ontology [Standards]

   12 citations · 2021
10. 10
    An Ontology Framework for Physics-Based Manipulation Planning

    12 citations · 2017

Key Collaborators

Contact & Links