Papers
12
Total Citations
113
H-Index
7
About
Saeed Mansouri is a leading roboticist whose work bridges the gap between theoretical optimization and real-world human-robot interaction. His primary research focuses on humanoid locomotion, rehabilitation robotics, and control systems for bipedal and exoskeletal robots. Mansouri’s most influential contribution is his 2015 work on optimal gait planning for humanoids on slippery surfaces, which has garnered 32 citations and fundamentally addressed how to minimize friction demands in 3D walking patterns. He further advanced the field by developing online path planning for the SURENA III humanoid using model predictive control, a method that reduced computational costs while enabling real-time motion generation. In recent years, Mansouri has made significant strides in assistive robotics, notably with his 2022 paper on sample-efficient policy adaptation for exoskeletons, which tackles the critical challenge of adapting control policies to different users and environments. His work on learning user-specific control policies using Gaussian process regression (2024) represents a cutting-edge approach to personalizing exoskeleton assistance. Beyond locomotion, Mansouri has explored medical robotics, including a hybrid algorithm for predicting heart motion in robotic-assisted beating-heart surgery. With over 100 total citations across his portfolio, Mansouri’s research is essential reading for anyone interested in the intersection of optimization, control theory, and human-centered robotics.
Research Focus
Key Achievements
Top Papers
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- 8Effect of step size and step period on feasible motion of a biped robot6 citations · 2010
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