Papers
7
Total Citations
78
H-Index
4
About
Pascal Seguin is a leading researcher in the field of robotic manipulation and bio-inspired locomotion, whose work bridges the gap between human motion and machine dexterity. His foundational research on parametric-based dynamic synthesis for 3D gait generation (2009, 33 citations) established a novel method for optimizing bipedal walking patterns by minimizing driving torques, directly contributing to the development of more human-like locomotion in robots. Seguin’s most impactful contributions, however, lie in dexterous manipulation. He led the mechatronic design of a novel tendon-driven robotic hand (2018, 20 citations), a human-sized hand capable of both adaptive grasping and fine in-hand manipulation—a rare achievement in robotics. His subsequent work on grasp quality criteria (2022, 13 citations) provides a systematic framework for evaluating and selecting optimal grasps, essential for advancing autonomous manipulation. Seguin has also pioneered methods for transferring human motion capture data to industrial robots (2015, 4 citations) and developed evaluation protocols for real in-hand dexterity (2022, 3 citations). His recent comprehensive reviews on flexible grippers (2023) synthesize decades of research, highlighting the evolution from specialized industrial grippers to versatile, human-like hands. With a career focused on making robots more adaptive and dexterous, Seguin’s work is instrumental for students and researchers aiming to create the next generation of assistive and industrial robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Parametric-based dynamic synthesis of 3D-gait33 citations · 2009
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- 4From Human Motion Capture to Industrial Robot Imitation4 citations · 2015
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