Force Feedback Model-Predictive Control via Online Estimation - Archive ouverte HAL Access content directly
Conference Papers Year : 2024

Force Feedback Model-Predictive Control via Online Estimation


Nonlinear model-predictive control has recently shown its practicability in robotics. However it remains limited in contact interaction tasks due to its inability to leverage sensed efforts. In this work, we propose a novel model-predictive control approach that incorporates direct feedback from force sensors while circumventing explicit modeling of the contact force evolution. Our approach is based on the online estimation of the discrepancy between the force predicted by the dynamics model and force measurements, combined with high-frequency nonlinear model-predictive control. We report an experimental validation on a torque-controlled manipulator in challenging tasks for which accurate force tracking is necessary. We show that a simple reformulation of the optimal control problem combined with standard estimation tools enables to achieve state-of-the-art performance in force control while preserving the benefits of model-predictive control, thereby outperforming traditional force control techniques. This work paves the way toward a more systematic integration of force sensors in model predictive control.
Fichier principal
Vignette du fichier
final_version.pdf (887.41 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-04564888 , version 1 (30-04-2024)


  • HAL Id : hal-04564888 , version 1


Armand Jordana, Sébastien Kleff, Justin Carpentier, Nicolas Mansard, Ludovic Righetti. Force Feedback Model-Predictive Control via Online Estimation. ICRA 2024 - IEEE International Conference on Robotics and Automation, May 2024, Yokohama, Japan. ⟨hal-04564888⟩
194 View
285 Download


Gmail Mastodon Facebook X LinkedIn More