The idea of extending human abilities by controlling robotic interfaces, such as extra fingers or arms, or by interacting with computers through neural signals, has long captured human imagination. Recent advances in neuroscience, robotics, and engineering have progressively transformed these concepts from science fiction into a concrete and rapidly emerging field of research known as human motor augmentation. One of the central challenges in this domain lies in identifying control policies that allow users to voluntarily and reliably operate external interfaces while remaining independent from, and non disruptive to, other physiological functions.

A prevailing perspective suggests that a promising solution may lie in exploiting the intrinsic redundancies of the human body, namely the fact that motor behavior is supported by more muscles, joints, and neurons than are strictly necessary to generate movement. This thesis investigates how such redundancies can be identified, evaluated, and leveraged to design control strategies for augmented motor control. Focusing on the problem of providing users with control over a third robotic arm, we first propose a human machine interface based on the voluntary modulation of the diaphragm as a kinematic null space control strategy. We then extend this approach to multi degree of freedom control by combining kinematic and muscular null space signals, specifically by integrating diaphragm modulation with contraction of the auricular muscles, vestigial muscles that represent an attractive and minimally interfering control source for augmentative devices.

Finally, this thesis introduces a novel framework grounded in recent advances in neural population dynamics and neural geometry. Using mesoscale neural signals recorded with electrocorticography, we demonstrate the possibility of identifying low dimensional neural spaces that selectively capture neural variance associated with motor imagery while remaining orthogonal to execution related dimensions. We propose that such imagery specific neural dimensions can be exploited to control external interfaces concurrently with overt movement while minimizing interference.

Overall, this thesis presents complementary kinematic, muscular, and neural strategies for motor augmentation, with a unifying emphasis on minimizing interference with concurrent biological functions. In doing so, it contributes both methodological frameworks and empirical evidence toward the development of robust and scalable augmentative technologies.