Introducing Force Feedback in Model Predictive Control

Sébastien Kleff (1, 2) , Ewen Dantec (2) , Guilhem Saurel (3) , Nicolas Mansard (2) , Ludovic Righetti (1, 4)

1 NYU Tandon School of Engineering
2 LAAS-GEPETTO - Équipe Mouvement des Systèmes Anthropomorphes
3 LAAS-IDEA - Service Informatique : Développement, Exploitation et Assistance
4 Max Planck Institute for Intelligent Systems [Tübingen]

In the literature about model predictive control (MPC), contact forces are planned rather than controlled. In this paper, we propose a novel paradigm to incorporate effort measurements into a predictive controller, hence allowing to control them by direct measurement feedback. We first demonstrate why the classical optimal control formulation, based on position and velocity state feedback, cannot handle direct feedback on force information. Following previous approaches in force control, we then propose to augment the classical formulations with a model of the robot actuation, which naturally allows to generate online trajectories that adapt to sensed position, velocity and torques. We propose a complete implementation of this idea on the upper part of a real humanoid robot, and show through hardware experiments that this new formulation incorporating effort feedback outperforms classical MPC in challenging tasks where physical interaction with the environment is crucial.

Domaines: Robotique [cs.RO]; Optimisation et contrôle [math.OC]

Sébastien Kleff, Ewen Dantec, Guilhem Saurel, Nicolas Mansard, Ludovic Righetti. Introducing Force Feedback in Model Predictive Control. IEEE International Conference on Intelligent Robots and Systems (IROS), Oct 2022, Kyoto, Japan. ⟨10.1109/IROS47612.2022.9982003⟩. ⟨hal-03594295v2⟩