abstract: Myoelectric control is lled with potential to signicantly change human-robot interaction. Humans desire compliant robots to safely interact in dynamic environments associated with daily activities. As surface electromyography non-invasively measures limb motion intent and correlates with joint stiness during co-contractions, it has been identied as a candidate for naturally controlling such robots. However, state-of-the-art myoelectric interfaces have struggled to achieve both enhanced functionality and long-term reliability. As demands in myoelectric interfaces trend toward simultaneous and proportional control of compliant robots, robust processing of multi-muscle coordinations, or synergies, plays a larger role in the success of the control scheme. This dissertation presents a framework enhancing the utility of myoelectric interfaces by exploiting motor skill learning and exible muscle synergies for reliable long-term simultaneous and proportional control of multifunctional compliant robots. The interface is learned as a new motor skill specic to the controller, providing long-term performance enhancements without requiring any retraining or recalibration of the system. Moreover, the framework oers control of both motion and stiness simultaneously for intuitive and compliant human-robot interaction. The framework is validated through a series of experiments characterizing motor learning properties and demonstrating control capabilities not seen previously in the literature. The results validate the approach as a viable option to remove the trade-o between functionality and reliability that have hindered state-of-the-art myoelectric interfaces. Thus, this research contributes to the expansion and enhancement of myoelectric controlled applications beyond commonly perceived anthropomorphic and \intuitive control" constraints and into more advanced robotic systems designed for everyday tasks. / Dissertation/Thesis / Doctoral Dissertation Mechanical Engineering 2015