Our research is aimed at understanding the sensory-motor system through close interaction with artificial systems. Specifically, we are interested in determining how the brain acquires, organizes and executes motor behaviors. We use robotic technologies to investigate how humans adapt to radical changes in body mechanics.
Consistent evidence indicates that the nervous system is capable of coping with changes in the body and in the environment by developing internal representations of relationship between control signals and their motor consequences. In this sense, motor learning is not only about improving performance. Motor learning is a means by which our brain develops an understanding of the physical and statistical properties of the world. We are studying the basic properties of this learning process and how it may be exploited to facilitate rehabilitation. Other studies within our group are directed at facilitating bidirectional communications between the human body and artificial instruments. We wish to combine the biological mechanisms of learning with machine learning algorithms for reducing the burden that patients are currently experiencing for the efficient operation of systems such as powered wheelchairs and other assistive devices. In a nutshell: we want to create systems that learn and adapt to their users.
Understanding how the brain controls motor behavior is of clinical interest since alterations in neuromotor control due to stroke and other neurological impairments can severely limit motor function. Through our research we wish to create knowledge that can help restore motor functions in individuals with neurological disorders.