Daulet Baimukashev
Papers
4
Total Citations
64
H-Index
4
About
Daulet Baimukashev is a robotics researcher whose work sits at the intersection of tactile sensing, deep learning, and control systems. His primary contributions lie in developing novel tactile sensing technologies for robotic manipulation, particularly through his work on an optical tactile sensor that can detect shear, torsion, and pressure—a significant advancement for dexterous object manipulation. This paper has garnered 30 citations, underscoring its impact on the field. Baimukashev has also designed a series elastic tactile sensing array for exploring both deformable and rigid objects, addressing critical gaps where vision-based sensors fall short. Beyond sensing hardware, he has applied deep learning to object recognition using synthetic depth scenes, enabling robots to grasp objects in cluttered environments. His work on approximate optimal control of a reaction-wheel-actuated spherical inverted pendulum further showcases his expertise in control theory, tackling challenges in variable impedance actuation for safe physical interaction. With a portfolio of highly cited papers, Baimukashev is advancing the capabilities of robots to perceive and interact with their environments through touch and intelligent control.
Research Focus
Key Achievements
Top Papers
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