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A. Del Prete
Researcher at University of Trento
Publications - 5
Citations - 443
A. Del Prete is an academic researcher from University of Trento. The author has contributed to research in topics: Robot & Humanoid robot. The author has an hindex of 4, co-authored 5 publications receiving 299 citations. Previous affiliations of A. Del Prete include Centre national de la recherche scientifique & University of Toulouse.
Papers
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Proceedings ArticleDOI
Whole-body model-predictive control applied to the HRP-2 humanoid
Jonas Koenemann,A. Del Prete,Yuval Tassa,Emanuel Todorov,Olivier Stasse,Maren Bennewitz,Nicolas Mansard +6 more
TL;DR: This paper implemented a complete model-predictive controller and applied it in real-time on the physical HRP-2 robot, the first time that such a whole-body model predictive controller is applied in real time on a complex dynamic robot.
Proceedings ArticleDOI
TALOS: A new humanoid research platform targeted for industrial applications
Olivier Stasse,Thomas Flayols,Rohan Budhiraja,Kevin Giraud-Esclasse,Justin Carpentier,Joseph Mirabel,A. Del Prete,Philippe Souères,Nicolas Mansard,Florent Lamiraux,Jean-Paul Laumond,L. Marchionni,H. Tome,F. Ferro +13 more
TL;DR: A new humanoid robot capable of interacting with a human environment and targeting industrial applications, equipped with torque sensors to measure joint effort and high resolution encoders to measure both motor and joint positions is introduced.
Proceedings ArticleDOI
Experimental evaluation of simple estimators for humanoid robots
Thomas Flayols,A. Del Prete,Patrick M. Wensing,Alexis Mifsud,Mehdi Benallegue,Olivier Stasse +5 more
TL;DR: Two new simplifications to the floating-base state estimation problem are proposed that make use of robust off-the-shelf orientation estimators to bootstrap development and are envisioned to help accelerate the development of baseline estimators in future humanoids.
Journal ArticleDOI
One Robot for Many Tasks: Versatile Co-Design Through Stochastic Programming
TL;DR: This letter considers the problems of designing a planar manipulator to transport a range of loads and a hopping monopod robot that must jump across a variety of terrains and details an approach to combine methods from stochastic programming with trajectory optimization to address the scalability of these multi-task co-design problems.
Proceedings ArticleDOI
Computational design of energy-efficient legged robots: Optimizing for size and actuators
TL;DR: This paper presents a computational framework for the design of high-performance legged robotic systems, and presents a novel approach to scale both the actuators and the robot structure in a way that ensures structural integrity by maintaining constant the normalized deflection of the links.