A model predictive control strategy to regulate movements and interactions

Takagi, Atsushi; Gomi, Hiroaki; Burdet, Etienne; Koike, Yasuharu

doi:10.1101/2022.08.24.505193

Cited by 5 publications

(7 citation statements)

References 52 publications

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“…In a recent work, the MPC framework was combined with an impedance controller to demonstrate its ability to handle nonlinear dynamics and changing environments (i.e. force fields) during movement [51]. Thanks to the recursive computations of the feedback gains inherent to this framework, Model Predictive Control was also used to investigate the control horizon in sensorimotor control [35][36][37].…”

Section: Plos Computational Biologymentioning

confidence: 99%

Continuous evaluation of cost-to-go for flexible reaching control and online decisions

De Comite,

Lefèvre,

Crevecoeur

2023

PLoS Comput Biol

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Humans consider the parameters linked to movement goal during reaching to adjust their control strategy online. Indeed, rapid changes in target structure or disturbances interfering with their initial plan elicit rapid changes in behavior. Here, we hypothesize that these changes could result from the continuous use of a decision variable combining motor and cognitive components. We combine an optimal feedback controller with a real-time evaluation of the expected cost-to-go, which considers target- and movement-related costs, in a common theoretical framework. This model reproduces human behaviors in presence of changes in the target structure occurring during movement and of online decisions to flexibly change target following external perturbations. It also predicts that the time taken to decide to select a novel goal after a perturbation depends on the amplitude of the disturbance and on the rewards of the different options, which is a direct result of the continuous monitoring of the cost-to-go. We show that this result was present in our previously collected dataset. Together our developments point towards a continuous evaluation of the cost-to-go during reaching to update control online and make efficient decisions about movement goal.

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Section: Plos Computational Biologymentioning

confidence: 99%

Continuous evaluation of cost-to-go for flexible reaching control and online decisions

De Comite,

Lefèvre,

Crevecoeur

2023

PLoS Comput Biol

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“…In other words, the presence of feedback does not automatically affect the feedforward control. The second one is to consider a model predictive control approach [54][55][56]. Intermittent feedback control could allow the system to re-estimate its current state and plan a feedforward motor command on a receding horizon [57].…”

Section: Discussionmentioning

confidence: 99%

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

Berret,

Verdel,

Burdet

et al. 2024

Preprint

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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.Author summaryThis work presents a computational framework for predicting how humans modulate muscle co-contraction to cope with uncertainties of different origins. In our neuromusculoskeletal system, uncertainties have both internal (sensorimotor noise) and external (environmental randomness) origins. The present study focuses on the latter type of uncertainty, which had not been dealt with systematically previously despite its importance in everyday life. Therefore, we thoroughly investigated how random disturbances occurring with some probability in a motor task shape the feedforward control of mechanical impedance through muscle co-contraction. Here we provide theoretical, numerical and experimental evidence that the optimal level of co-contraction steeply increases with the uncertainty of our environment. These findings show that muscle co-contraction embodies uncertainty and optimally mitigates its consequences on task execution when feedback control is insufficient due to sensory noise and delays.

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“…In a recent work, this framework was combined with an impedance controller to demonstrate its ability to handle nonlinear dynamics and changing environments (i.e. force fields) during movement (45). Here, we combined this MPC framework with the continuous evaluation of the cost-to-go function by generalizing the previous implementation (25) proposed by Nashed and colleagues by integrating the reward within the cost-to-go function through a positive cost bias for the less rewarding options.…”

Section: Discussionmentioning

confidence: 99%

Continuous monitoring of cost-to-go for flexible reaching control and online decisions

Comite

Lefèvre

Crevecoeur

2022

Preprint

View full text Add to dashboard Cite

Humans consider the parameters linked to movement goal during reaching to adjust their control strategy online. Indeed, rapid changes in target structure or disturbances interfering with their initial plan elicit rapid changes in behavior. Here, we hypothesize that these changes could result from the continuous use of a decision variable combining motor and cognitive components. We combine an optimal feedback controller with a real-time monitoring of the expected cost-to-go, which considers target- and movement-related costs, in a novel theoretical framework. This model reproduces reaching behaviors in presence of changes in the target structure occurring during movement and of online decisions to flexibly change target following external perturbations. It also predicts that the time taken to decide to select a novel goal after a perturbation depends on the amplitude of the disturbance and on the rewards of the different options, which is a direct result of the continuous monitoring of the cost-to-go. We show that this result was present in our previously collected dataset. Together our developments point towards a continuous monitoring of the cost-to-go during reaching to update control online and make efficient decisions about movement goal.

show abstract

A model predictive control strategy to regulate movements and interactions

Cited by 5 publications

References 52 publications

Continuous evaluation of cost-to-go for flexible reaching control and online decisions

Continuous evaluation of cost-to-go for flexible reaching control and online decisions

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

Continuous monitoring of cost-to-go for flexible reaching control and online decisions

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