Victor H. Giraud
Papers
2
Total Citations
7
H-Index
2
About
Victor H. Giraud is a roboticist whose research focuses on the frontier of deformable object manipulation and skill learning for industrial automation. His work addresses one of robotics’ most persistent challenges: enabling robots to handle soft, flexible, and complex objects that resist traditional rigid-body modeling. Giraud’s major contributions include the development of **dual quaternion-based dynamic movement primitives (DMPs)** for learning industrial tasks via teleoperation, a method that effectively transfers human manipulation skills—particularly for deformable objects—to robotic systems. He has also advanced **optimal shape servoing** with task-focused convergence constraints, proposing control strategies that not only guide a deformable object to a desired final shape but also ensure it follows a specific deformation path, a critical capability for precision manufacturing. His most-cited work (4 citations) on DMPs for industrial teleoperation and his shape servoing paper (3 citations) represent foundational steps toward automating tasks that currently require human dexterity. Giraud’s research is particularly notable for bridging the gap between human demonstration and robust robotic execution, with direct applications in assembly, food handling, and textile manipulation. His work is essential reading for researchers in robot learning, control theory, and industrial automation.
Research Focus
Key Achievements
Top Papers
- 1
- 2Optimal Shape Servoing with Task-focused Convergence Constraints3 citations · 2022