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Human-inspired sensorimotor controller for dynamic motion adaptation: a study in robotic arms.

Maxime Marchal^1,2, Raphaël Furnémont^1,2, Tom Verstraten^1,2

¹Brubotics, Vrije Universiteit Brussel, Elsene, Belgium.

Frontiers in Neurorobotics

|June 8, 2026

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This study introduces a human-inspired sensorimotor learning controller for robotic arms, enabling adaptation without precise dynamic models. The approach learns from experience, improving robotic control in dynamic environments.

Area of Science:

Robotics
Control Systems
Machine Learning

Background:

Robotic systems in dynamic environments face challenges adapting movements to sensory signals without explicit models.
Limited or unreliable dynamic models and positional feedback hinder conventional control schemes.
Need for control architectures that learn and generalize from experience, mimicking human sensorimotor systems.

Purpose of the Study:

To present a human-inspired controller for articulated robotic arms based on sensorimotor learning.
To enable adaptability to dynamic tasks and external stimuli without precise analytical dynamic models.
To account for gravity and system dynamics through empirical learning.

Main Methods:

An offline exploration phase builds a discretized database of system responses from sampled states and actuator commands.

Keywords:

biomimetic control dynamic task adaptation human-inspired controller robotic arm control sensorimotor learning

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An online exploitation phase matches current states to the closest explored state for control action selection.

A probabilistic command-selection rule and empirical state-action-response relationships are utilized.

Main Results:

Demonstrated feasibility on a 2-DOF RR robotic arm with dynamic trajectories (sinusoidal, trapezoidal).
Analyzed the influence of control time parameters and actuator friction.
Investigated controller redeployability to modified mechanical configurations after re-exploration.

Conclusions:

Biologically inspired learning mechanisms show potential for data-driven robotic control.
Highlighted limitations including discretization, scalability, and future online adaptation needs.
The framework offers an alternative to model-based control for adaptable robotic systems.