Our research examines how humans learn models of the external world and use these to adapt our movements to new experiences. A major focus of this research is how we adapt to instability in the environment through co-activation of our muscles to control the endpoint stiffness of our limbs. For example, one of our papers has proposed an algorithm which the brain may use to solve the problem of adaptation to novel dynamics such that a solution is found, robust to both noise and instability, which minimizes the metabolic cost (Franklin et al., 2008). By investigating changes in trajectories, endpoint stiffness, and electrical activity of the muscles, we hope to elucidate the underlying mechanisms by which the brain learns new tasks.


The methods by which humans solve these problems can then be utilized by robots in the future to produce similar adaptation and robustness to an externally changing world. The same knowledge can be used for the design of prosthetic hands and limbs to try to restore motor function that have been lost. Similarly, once we understand the mechanisms underlying learning and adpatation in healthy individuals, we may be able to develop novel training technqiues that can be used for rehabilitation, for example after stroke injury. However the benefit of any application of such techniques will depend on the functions of the damaged areas of the brain.