Stochastic Optimal Feedforward-Feedback Control for Partially Observable Sensorimotor Systems

TL;DR

This paper develops a new control framework that optimally combines feedforward and feedback strategies for complex, uncertain systems, with applications to human neuromechanics and insights into muscle co-contraction.

Contribution

It introduces a novel stochastic optimal control method that explicitly accounts for feedback uncertainties and latencies in nonlinear, high-dimensional systems.

Findings

Muscle co-contraction emerges as an optimal response to sensorimotor uncertainties.

The framework provides theoretical guarantees on approximation accuracy.

Application to human neuromechanics demonstrates biological plausibility.

Abstract

Robust control of complex engineered and biological systems hinges on the integration of feedforward and feedback mechanisms. This is exemplified in neural motor control, where feedforward muscle co-contraction complements sensory-driven feedback corrections to ensure stable behaviors. However, deriving a general continuous-time framework to determine such optimal control policies for partially observable, stochastic, nonlinear, and high-dimensional systems remains a formidable computational challenge. Here, we introduce a framework that extends neighboring optimal control by enabling the feedforward plan to explicitly account for feedback uncertainties and latencies. Using statistical linearization, we transform the stochastic problem into an approximately equivalent deterministic optimization within a tractable, augmented state space that retains critical nonlinearities, offering both mechanistic interpretability and theoretical guarantees on approximation fidelity. We apply this framework to human neuromechanics, demonstrating that muscle co-contraction emerges as an optimal adaptation to task demands, given the characteristics of our sensorimotor system. Our results provide a computational foundation for neuromotor control and a generalizable tool for the control of nonlinear stochastic systems.