Fares J. Abu‐Dakka

About

Fares J. Abu-Dakka is a prominent robotics researcher whose work spans robot learning, imitation learning, manipulation skills, and soft robotics. He has made particularly significant contributions to the study of Dynamic Movement Primitives (DMPs), authoring a widely acclaimed tutorial survey on the topic that has accumulated over 200 citations since 2023, establishing itself as an essential reference for researchers entering the field. His early work on programming by demonstration — teaching robots complex assembly tasks such as peg-in-hole insertion through human-guided kinesthetic demonstrations — laid important groundwork for practical skill transfer in industrial robotics, earning nearly 150 citations for his force-profile adaptation methods. Abu-Dakka has also advanced the theoretical foundations of imitation learning in Cartesian and orientation spaces, including geometry-aware approaches that respect the mathematical structure of robot control variables. Beyond classical manipulation, he has contributed to the emerging field of soft robotics, co-developing innovative biomimetic hand designs for both terrestrial and underwater grasping applications. His more recent research into variable impedance learning control reflects a sustained commitment to achieving human-like dexterity in robotic systems. Collectively, his body of work bridges biological inspiration and rigorous engineering, making him an influential voice in modern robot learning and manipulation research.

Research Focus

Key Achievements

Top Papers

1. 1
   Dynamic movement primitives in robotics: A tutorial survey

   205 citations · 2023
2. 2
   Adaptation of manipulation skills in physical contact with the environment to reference force profiles

   148 citations · 2015
3. 3
   Solving peg-in-hole tasks by human demonstration and exception strategies

   78 citations · 2014
4. 4
   A Novel Soft Robotic Hand Design With Human-Inspired Soft Palm: Achieving a Great Diversity of Grasps

   69 citations · 2021
5. 5
   Toward Orientation Learning and Adaptation in Cartesian Space

   62 citations · 2020
6. 6
   Geometry-aware Dynamic Movement Primitives

   47 citations · 2020
7. 7
   Dynamic Movement Primitives in Robotics: A Tutorial Survey

   46 citations · 2021
8. 8
   A Bidirectional Soft Biomimetic Hand Driven by Water Hydraulic for Dexterous Underwater Grasping

   43 citations · 2022
9. 9
   Model-based variable impedance learning control for robotic manipulation

   37 citations · 2023
10. 10
    Transfer of assembly operations to new workpiece poses by adaptation to the desired force profile

    37 citations · 2013

Key Collaborators

Contact & Links