Force Feedback in Model-Predictive Control: A Soft Contact Approach
Résumé
Model-predictive control is an appealing framework to control robots due to its ability to exploit both sensory information and model predictions. But its performance remains fundamentally limited in tasks involving contact with the environment, in part because optimal control policies do not reason over force measurements. In this article, we propose a first complete answer to this issue by introducing a novel approach to perform force feedback in model-predictive control. We propose to augment the state-space with a visco-elastic model of the contact force in the task space in order to systematically include measured efforts into the optimal control loop. We derive a complete predictive controller with an efficient formulation whose implementation is released in open-source. We demonstrate through simulation studies and hardware experiments that our approach enables to combine the benefits of force control and model-predictive control within a single architecture, thereby outperforming existing approaches in challenging contact tasks.
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