Force Feedback Model-Predictive Control via Online Estimation

Armand Jordana, Sebastien Kleff, Justin Carpentier, Nicolas Mansard, Ludovic Righetti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication2024 IEEE International Conference on Robotics and Automation, ICRA 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages11503-11509
Number of pages7
ISBN (Electronic)9798350384574
DOIs
StatePublished - 2024
Event2024 IEEE International Conference on Robotics and Automation, ICRA 2024 - Yokohama, Japan
Duration: May 13 2024May 17 2024

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2024 IEEE International Conference on Robotics and Automation, ICRA 2024
Country/TerritoryJapan
CityYokohama
Period5/13/245/17/24

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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