Papers
14
Total Citations
124
H-Index
7
About
Salah Bazzi is a researcher working at the intersection of human motor control, robotics, and dynamical systems, with a particular focus on how humans and robots manipulate objects with complex, nonlinear, and underactuated dynamics. His most recognized work investigates the remarkable human ability to handle dynamically challenging objects — such as carrying a cup of coffee — and seeks to translate these biological insights into improved robotic control architectures. Across his most-cited publications, Bazzi has demonstrated that humans exploit stability properties, simplified internal models, and strategic initial conditions to manage otherwise computationally intractable control problems, findings that have accumulated over 100 citations collectively. His 2021 paper on pre-movement preparation and his 2020 contributions on robustness and control contraction metrics represent particularly influential advances, bridging neuroscience and engineering. Beyond manipulation, Bazzi has extended his work into mobile robotics, developing probabilistic motion models for skid-steer robots navigating challenging off-road terrains. His research on ergonomic human-robot handovers further reflects a commitment to safe, human-centered collaborative robotics. Overall, Bazzi's work offers a compelling framework for designing robots that learn from human dexterity to achieve more adaptive, robust real-world performance.
Research Focus
Key Achievements
Top Papers
- 1
- 2Human control of complex objects: towards more dexterous robots17 citations · 2020
- 3
- 4Dynamic Primitives in Human Manipulation of Non-Rigid Objects12 citations · 2019
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- 6Transient Behavior and Predictability in Manipulating Complex Objects8 citations · 2020
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- 10Simplified internal models in human control of complex objects5 citations · 2024