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Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

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Published on: May 8, 2021

Human motor learning is robust to control-dependent noise.

Bo Pang¹, Leilei Cui², Zhong-Ping Jiang²

¹Department of Electrical and Computer Engineering, New York University, 370 Jay Street, Brooklyn, NY, 11201, USA. bo.pang@nyu.edu.

Biological Cybernetics

|March 3, 2022

Summary

Human motor learning remains robust despite noisy sensorimotor information. A new computational model shows how the central nervous system (CNS) learns effectively even with imprecise data, converging towards optimal control policies.

Keywords:

Arm reaching Policy iteration Reinforcement learning Robustness Sensorimotor control

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Area of Science:

Neuroscience
Computational Neuroscience
Robotics

Background:

Sensorimotor interactions are inherently noisy, impacting motor learning.
The central nervous system (CNS) must effectively learn from imprecise sensory information.

Purpose of the Study:

To develop a computational mechanism explaining motor learning robustness to control-dependent noise.
To investigate how the CNS manages imprecise information during motor learning.

Main Methods:

Integrating reinforcement learning and adaptive optimal control principles.
Developing a model-free control mechanism that synthesizes policies from noisy sensory data.
Analyzing policy convergence mathematically under mild conditions.

Main Results:

The proposed mechanism demonstrates robust motor learning despite imprecise sensory data.
Policies provably converge to a neighborhood of the optimal policy with increasing trials.
Computational results align with experimental observations in reaching tasks.

Conclusions:

The model-free control principle explains the inherent robustness of human sensorimotor systems to noise.
This mechanism offers insights into how the CNS adapts motor control with imprecise feedback.