A computational theory for the production of limb movements.

Guigon, Emmanuel

doi:10.1037/rev0000323

Cited by 9 publications

(21 citation statements)

References 186 publications

(463 reference statements)

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“…Many studies have used optimal control theory to model a myriad of individual motor tasks (e.g., [61][62][63][64][65]). Such efforts, however, did not consider tasks where the task goals and/or movement objectives change as the task is being performed.…”

Section: Discussionmentioning

confidence: 99%

Adaptive Multi-Objective Control Explains How Humans Make Lateral Maneuvers While Walking

Desmet

Cusumano

Dingwell

2022

Preprint

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To successfully traverse their environment, humans must often perform maneuvers to achieve desired task goals while simultaneously maintaining balance. Humans accomplish these tasks primarily by modulating their foot placements. As humans are more unstable laterally, we must better understand how humans modulate lateral foot placement. We previously developed a theoretical framework and corresponding computational models to describe how humans regulate lateral stepping during straight-ahead continuous walking. This framework yields goal functions for step width and lateral body position that define the walking task and determine the set of all possible task solutions as Goal Equivalent Manifolds (GEMs). Here, we used this framework to determine if humans can regulate lateral stepping during non-steady-state lateral maneuvers by minimizing errors in accordance with these goal functions. Twenty young healthy adults each performed four lateral lane-change maneuvers in a virtual reality environment. Within our general lateral stepping regulation framework, we first re-examined the requirements of such transient walking tasks. Doing so yielded new theoretical predictions regarding how steps during any such maneuver should be regulated to minimize error costs, consistent with the goals required at each step and with how these costs are adapted at each step during the maneuver. Humans performed the experimental lateral maneuvers in a manner consistent with our theoretical predictions. Furthermore, their stepping behavior was well modeled by merely adapting the parameters of our previous lateral stepping models from step to step. To our knowledge, our results are the first to demonstrate humans can use evolving cost landscapes in real time to perform such an adaptive motor task and, furthermore, that such adaptation can occur quickly – over only one step. Thus, the predictive capabilities of our general stepping regulation framework extend to a much greater range of walking tasks beyond just normal, straight-ahead walking.

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Section: Discussionmentioning

confidence: 99%

Adaptive Multi-Objective Control Explains How Humans Make Lateral Maneuvers While Walking

Desmet

Cusumano

Dingwell

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…We define a state vector and rewrite the dynamics (Eqs 1 and 3 ) as for the unperturbed dynamics or for the perturbed dynamics, where n dyn is additive noise on the dynamics. We formulate an optimal feedback control problem for this dynamics as a search for a control policy u ( t ) to reach a goal while minimizing the cost where F • is either F 0 or F φ to indicate whether optimization applies to the unperturbed or the perturbed dynamics, and T H is the planning horizon [ 24 ]. In [ 8 ], optimization runs on a fixed duration (0.5 s) and thus cannot be used to simulate before-effect and after-effect conditions which require flexible time to produce online movement corrections.…”

Section: Methodsmentioning

confidence: 99%

“…In [ 8 ], optimization runs on a fixed duration (0.5 s) and thus cannot be used to simulate before-effect and after-effect conditions which require flexible time to produce online movement corrections. Control with a planning horizon offers an efficient solution to time flexibility as at any time and in any changing situation due to a perturbation there always remains the duration of a planning horizon to reach designated goals [ 24 ]. The initial boundary condition is given by , where is the estimated value of X ( t ) provided by an optimal state estimator using forward modeling and delayed sensory feedback with delay Δ [ 24 , 44 ].…”

Section: Methodsmentioning

confidence: 99%

“…Control with a planning horizon offers an efficient solution to time flexibility as at any time and in any changing situation due to a perturbation there always remains the duration of a planning horizon to reach designated goals [ 24 ]. The initial boundary condition is given by , where is the estimated value of X ( t ) provided by an optimal state estimator using forward modeling and delayed sensory feedback with delay Δ [ 24 , 44 ]. The state estimator is given by where is the dynamics for estimation which is either F 0 or F φ (see below), is a 4×8 observation matrix ( is the 4×4 identity and the 4×4 null matrix), indicating that only the position and velocity are observed, K ( t ) the Kalman gain and where n obs is additive observation noise.…”

Section: Methodsmentioning

confidence: 99%

“…The redirection model is taken from [ 24 ] and customized to the current formulation. The baseline and before-effect conditions are the same as for the reoptimization model.…”

Section: Methodsmentioning

confidence: 99%

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What is the nature of motor adaptation to dynamic perturbations?

2022

Self Cite

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When human participants repeatedly encounter a velocity-dependent force field that distorts their movement trajectories, they adapt their motor behavior to recover straight trajectories. Computational models suggest that adaptation to a force field occurs at the action selection level through changes in the mapping between goals and actions. The quantitative prediction from these models indicates that early perturbed trajectories before adaptation and late unperturbed trajectories after adaptation should have opposite curvature, i.e. one being a mirror image of the other. We tested these predictions in a human adaptation experiment and we found that the expected mirror organization was either absent or much weaker than predicted by the models. These results are incompatible with adaptation occurring at the action selection level but compatible with adaptation occurring at the goal selection level, as if adaptation corresponds to aiming toward spatially remapped targets.

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A model predictive control strategy to regulate movements and interactions

Takagi¹,

Gomi²,

Burdet

et al. 2022

Preprint

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Humans are adept at moving the arm to interact with objects and surfaces. The brain is thought to regulate motion and interactions using two different controllers, one specialized for movements and the other for force regulation. However, it remains unclear whether different control mechanisms are necessary. Here we show that the brain can employ a single high-level control strategy for both movement and interaction control. The Model Predictive Control (MPC) strategy introduced in this paper uses an internal model of the environment to plan the arm's muscle activity whilst updating its predictions using periodic feedback. Computer simulations demonstrate MPC's ability to produce human-like movements and after-effects in free and force field environments. It can simultaneously regulate both force and stiffness during interactions, and can accomplish motor tasks demanding transitions between motion and interaction control. Model Predictive Control promises to be an important tool to test ideas of motor control as it can handle nonlinear dynamics with changing environments and goals without having to specify the movement duration.

show abstract

A computational theory for the production of limb movements.

Cited by 9 publications

References 186 publications

Adaptive Multi-Objective Control Explains How Humans Make Lateral Maneuvers While Walking

Adaptive Multi-Objective Control Explains How Humans Make Lateral Maneuvers While Walking

What is the nature of motor adaptation to dynamic perturbations?

A model predictive control strategy to regulate movements and interactions

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