Victor H. Giraud — accepted AI-conference papers

2023

Dual quaternion based dynamic movement primitives to learn industrial tasks using teleoperation

Rohit Chandra, Victor H. Giraud, Mohammad Alkhatib, Youcef Mezouar

ICRA 2023poster

Dynamic movement primitives (DMPs) provide an effective method of learning manipulation skills from human demonstration. DMPs can be especially useful for imitating industrial manipulation tasks which are performed by humans and are difficult to model, for instance, deformable object manipulation. I…

Cited by 4 Source ↗Scholar ↗

2022

Optimal Shape Servoing with Task-focused Convergence Constraints

Victor H. Giraud, Maxime Padrin, Mohammadreza Shetab-Bushehri, Chedli Bouzgarrou, Youcef Mezouar, Erol Ozgur

IROS 2022poster

Most deformable object manipulation tasks still rely on skillful human operators. To automate such tasks, a robotic system should not only be able to deform an object to a desired shape but also servo its deformation along a specific path towards the desired shape. We propose a shape servoing contro…

Cited by 2 Source ↗Scholar ↗