AIConfPaper
← Search

Arthur Haffemayer

5 accepted papers

2026

Infinite-Horizon Value Function Approximation for Model Predictive Control

Armand Jordana, Sebastien Kleff, Arthur Haffemayer, Joaquim Ortiz-Haro, Justin Carpentier, Nicolas Mansard +1
ICRA 2026poster

Model Predictive Control has emerged as a popular tool for robots to generate complex motions. However, the real-time requirement has limited the use of hard constraints and large preview horizons, which are necessary to ensure safety and stability. In practice, practitioners have to carefully desig…

Cited by 0Source ↗PDF ↗Scholar ↗
2026

Learning-Guided Force-Feedback Model Predictive Control with Obstacle Avoidance for Robotic Deburring

Krzysztof Wojciechowski, Ege Gursoy, Arthur Haffemayer, Sebastien Kleff, Vincent Bonnet, Florent Lamiraux +1
ICRA 2026poster

Model Predictive Control (MPC) is widely used for torque-controlled robots, but classical formulations often neglect real-time force feedback and struggle with contact-rich industrial tasks under collision constraints. Deburring in particular requires precise tool insertion, stable force regulation,…

Cited by 0Source ↗PDF ↗Scholar ↗
2026

Warm-Starting Collision-Free Model Predictive Control With Object-Centric Diffusion

Arthur Haffemayer, Alexandre Chapin, Armand Jordana, Krzysztof Wojciechowski, Florent Lamiraux, Nicolas Mansard +1
RA-L 2026

Acting in cluttered environments requires predicting and avoiding collisions while still achieving precise control. Conventional optimization-based controllers can enforce physical constraints, but they struggle to produce feasible solutions quickly when many obstacles are present. Diffusion models

Cited by 1Source ↗Scholar ↗
2025

Collision Avoidance in Model Predictive Control Using Velocity Damper

Arthur Haffemayer, Armand Jordana, Ludovic De Matteïs, Krzysztof Wojciechowski, Ludovic Righetti, Florent Lamiraux +1
ICRA 2025

<div> We propose an advanced method for controlling the motion of a manipulator robot with strict collision avoidance in dynamic environments, leveraging a velocity damper constraint. Unlike conventional distance-based constraints, which tend to saturate near obstacles to reach optimality, the veloc

Cited by 4Source ↗Scholar ↗
2025

Infinite-Horizon Value Function Approximation for Model Predictive Control

Armand Jordana, Sébastien Kleff, Arthur Haffemayer, Joaquim Ortiz de Haro, Justin Carpentier, Nicolas Mansard +1
RA-L 2025

Model Predictive Control has emerged as a popular tool for robots to generate complex motions. However, the real-time requirement has limited the use of hard constraints and large preview horizons, which are necessary to ensure safety and stability. In practice, practitioners have to carefully desig

Cited by 6Source ↗Scholar ↗