CRCNS US-French Research Proposal: Optimal Control Framework for Studying the Neural Control of Force, Impedance, and Feedback Responses

A defining characteristic of human motor behavior is the ability to effectively control movements during physical interactions with the environment. This remarkable capacity depends on the nervous system’s ability to integrate multiple control policies, including force control, impedance control, and feedback control. However, the underlying neuromuscular mechanisms that facilitate such robust and skillful movement control in new environments are still not well understood. This Collaborative Research in Computational Neuroscience (CRCNS) project seeks to elucidate how the human brain and body coordinate movement strategies that combine force, impedance, and feedback control during physical interactions. Gaining insight into these processes will yield significant benefits by advancing foundational neuroscience, enhancing the design of wearable robotics and assistive technologies, and informing innovative approaches to neurorehabilitation for individuals with motor impairments. Furthermore, the project promotes international collaboration between research groups in the United States and France while providing hands-on training opportunities for graduate students and postdoctoral researchers in experimental neuroscience, robotics, and computational modeling. By deepening knowledge at the intersection of neuroscience and engineering, this work aligns with national priorities around automation, health, scientific innovation, and education. This research develops and tests a novel theory of neuromuscular control that predicts how humans coordinate force, impedance, and feedback responses during physically interactive tasks. The approach integrates three key elements: (1) an optimal control framework capable of modeling coordinated force and stiffness control; (2) the incorporation of a computationally efficient muscle model that captures the mechanical properties of muscle force and impedance within the optimal control framework; and (3) the experimental validation of model predictions through experiments that merge robotics with functional MRI to investigate the neuromuscular control involved in tasks requiring physical interaction. Model predictions will inform the design of behavioral experiments where participants are subjected to controlled dynamic perturbations while performing movements primarily involving the wrist joint, both inside and outside the scanner. These experiments aim to test hypotheses regarding the differential expression of specific neuromuscular strategies and identify their neural correlates. By linking computational predictions to both muscular and brain activity, this project will elucidate how the nervous system flexibly deploys distinct control policies in response to varying task demands. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.