Papers
7
Total Citations
137
H-Index
5
About
Marek Sierotowicz is a leading researcher at the intersection of rehabilitation robotics, haptic telemanipulation, and human-robot interaction. His work centers on developing intuitive, safe, and transparent control systems for assistive and space robotics, with a particular focus on soft exoskeletons and variable impedance technologies. His most cited paper (70 citations) introduces an EMG-driven machine learning framework for controlling a soft glove designed for grasping assistance and rehabilitation, addressing the critical trade-off between compliance and control precision. Sierotowicz has also pioneered the concept of model-augmented haptic telemanipulation (20 citations), which overcomes challenges like communication delays in space telerobotics. His contributions extend to low-cost wearable posture tracking for rehabilitation and prosthetics (17 citations), as well as deflection-domain passivity control for variable stiffness systems (14 citations). Notably, he has explored learning-based teleoperation of bimanual humanoid robots for daily-living tasks and the use of functional electrical stimulation to emulate human impedance control. Through his work, Sierotowicz advances safe, human-in-the-loop robotic systems that bridge the gap between compliant hardware and precise control, with applications ranging from neurorehabilitation to extravehicular activity.
Research Focus
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Top Papers
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