sensorimotori

Sensorimotori is a term used primarily in neurobiology and robotics to describe the integration of sensory perception and motor control within a biological or artificial system. In the animal nervous system, sensorimotor circuits receive input from peripheral sensory receptors—such as proprioceptors, touch, vision, and auditory sensors—and translate that information into coordinated motor output via motor neurons and muscle fibers. This integration allows organisms to perform complex movements, maintain balance, and adaptively respond to environmental changes.

In computational models, the sensorimotor framework has been formalized through hierarchical control architectures that include sensory layers, internal state models, and motor command generators. Model‑based approaches, such as those used in motor learning and reinforcement learning algorithms, emphasize predictive coding and forward models that estimate the consequences of motor commands on sensory states. These models support the design of robotic systems that learn to manipulate objects, navigate variable terrains, or recover from disturbances in a manner analogous to biological organisms.

Clinical research has shown that disruptions in sensorimotor processing underlie a range of neurological disorders, including Parkinson’s disease, amyotrophic lateral sclerosis, and stroke recovery deficits. Rehabilitation strategies often target sensorimotor integration by pairing sensory stimulation with motor training to promote cortical reorganization and functional recovery.

Because sensorimotor interactions are fundamental to both living and artificial agents, interdisciplinary studies spanning neuroscience, computer science, and robotics continue to refine our understanding of how perception and action are coordinated and how this knowledge may be harnessed for advanced prosthetics, neuroprosthetics, and autonomous systems.