Mohammad Khansari
Stanford University, École Polytechnique Fédérale de Lausanne
Papers
2
Total Citations
60
H-Index
2
About
Mohammad Khansari’s research lies at the intersection of robotics, control theory, and human-inspired manipulation, with a focus on enabling robots to perform complex physical interactions under uncertainty. His major contributions center on developing adaptive control frameworks that integrate motion generation with impedance control, allowing robots to handle surface–surface contact tasks—such as placing a box on a table—with human-like compliance. In his 2016 work on adaptive human-inspired compliant contact primitives (34 citations), Khansari devised a control policy that accounts for partial knowledge of object shape and environmental uncertainty, significantly advancing robotic dexterity in real-world settings. His 2014 paper on modeling discrete movements with state-varying stiffness and damping (26 citations) introduced a unified framework that bridges motion generation and interaction control, a traditionally separate challenge in robotics. These contributions have practical implications for manufacturing, assistive robotics, and autonomous manipulation. Khansari’s work is notable for its biologically inspired approach, translating human motor strategies into robust robotic algorithms, and his publications continue to influence researchers in adaptive control and compliant manipulation.
Research Focus
Key Achievements
Top Papers
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