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Preprints, Working Papers, ... Year : 2024

Co-Contraction Embodies Uncertainty: An Optimal Feedforward Strategy for Robust Motor Control

Dorian Verdel
Etienne Burdet
Frédéric Jean

Abstract

Despite our environment is often uncertain, we generally manage to generate stable motor behaviors. While reactive control plays a major role in this achievement, proactive control is critical to cope with the substantial noise and delays that affect neuromusculoskeletal systems. In particular, muscle co-contraction is exploited to robustify feedforward motor commands against internal sensorimotor noise as was revealed by stochastic optimal open-loop control modeling. Here, we extend this framework to neuromusculoskeletal systems subjected to random disturbances originating from the environment. The analytical derivation and numerical simulations predict a singular relationship between the degree of uncertainty in the task at hand and the optimal level of anticipatory co-contraction. This prediction is confirmed through a single-joint pointing task experiment where an external torque is applied to the wrist near the end of the reaching movement with varying probabilities across blocks of trials. We conclude that uncertainty calls for impedance control via proactive muscle co-contraction to stabilize behaviors when reactive control is insufficient for task success.
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hal-04615170 , version 1 (18-06-2024)

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Bastien Berret, Dorian Verdel, Etienne Burdet, Frédéric Jean. Co-Contraction Embodies Uncertainty: An Optimal Feedforward Strategy for Robust Motor Control. 2024. ⟨hal-04615170⟩
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