Papers
44
Total Citations
477
H-Index
11
About
Mathieu Grossard is a prominent robotics researcher whose work spans advanced motion control, human-robot interaction, and dexterous robotic manipulation. His research has made significant contributions to the fields of flexible-joint robot control, safe physical human-robot interaction, and anthropomorphic hand design, establishing him as a key figure in modern robotics engineering. Among his most impactful contributions is his development of preview H∞ control strategies for elastic-joint robots under model uncertainties (85 citations), which introduced novel model-based identification and control design methods that advanced precision motion control. His design of a fully modular, backdrivable dexterous hand (64 citations) demonstrated how biomechanical insights can inspire sophisticated robotic grasping systems. Equally notable is his pioneering work on adaptive filtering for robot impact detection (48 citations), enabling safer human-robot collaboration through robust proprioceptive sensing under real-world modeling imperfections. Grossard has also applied machine learning to classify human-robot contact situations (29 citations) and edited the comprehensive volume *Flexible Robotics* (2013), reflecting his broad scholarly influence. Across his body of work, his research consistently bridges theoretical control design with practical implementation, making his contributions invaluable to researchers and engineers developing the next generation of safe, intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Design of a fully modular and backdrivable dexterous hand64 citations · 2014
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- 4Using Neural Networks for Classifying Human-Robot Contact Situations29 citations · 2019
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- 8Flexible Robotics19 citations · 2013
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