Myoelectric control has seen decades of research as a potential interface between human and machines. High-density surface electromyography (HDsEMG) non-invasively provides a rich set of signals representing underlying muscle contractions and, at a higher level, human motion intent. Many pattern recognition techniques have been proposed to predict motions based on these signals. However, control schemes incorporating pattern recognition struggle with long-term reliability due to signal stochasticity and transient changes. This study proposes an alternative approach for HDsEMG-based interfaces using concepts of motor skill learning and muscle synergies to address long-term reliability. Muscle synergy-inspired decomposition reduces HDsEMG into control inputs robust to small electrode displacements. The novel control scheme provides simultaneous and proportional control, and is learned by the subject simply by interacting with the device. In a multiple-day experiment, subjects learned to control a virtual 7-DoF myoelectric interface, displaying performance learning curves consistent with motor skill learning. On a separate day, subjects intuitively transferred this learning to demonstrate precision tasks with a 7-DoF robot arm, without requiring any recalibration. These results suggest that the proposed method may be a practical alternative to pattern recognition-based control for long-term use of myoelectric interfaces.