The rise of robotic body augmentation brings forth new developments that will transform robotics, human-machine interaction, and wearable electronics. Extra robotic limbs, although building upon restorative technologies, bring their own set of challenges in achieving effective bidirectional human-machine collaboration. The questions are whether people can adjust and learn to use a new robotic limb and whether this is achievable without limiting their other physical capabilities. In realizing successful robotic body augmentation, it's crucial to make sure that introducing an extra (artificial) limb doesn't compromise the functions of a natural (biological) limb. This thesis presents research on robotic body augmentation via extra robotic limbs, merging the definition of theoretical foundations with empirical investigations on the adaptability of the human body and brain to advanced technological integrations. Central to this work is the concept of the 'Neural Resource Allocation Problem', defined and discussed in the introduction of this thesis. It addresses the challenges of integrating augmentative devices with the human body without compromising natural functionalities. Such conceptualization is crucial to ensure that augmentation technologies effectively expand user's capacities rather than simply rerouting resources and replacing an existing function with a different, new one. Based on this theoretical groundwork, I then proposed operational guidelines and detailed the development and characterization of an ad-hoc human-machine interface based on gaze and diaphragmatic respiration for extra robotic arms. The validation carried out on a virtual extra arm thanks to the neuro-robotic platform engineered for this work and the subsequent testing with an extra robotic arm proved the proposed human-machine interface to be effective and non-intrusive, substantiating the proposed methodology. The in-depth analysis of how users adapt to a toe-controlled robotic thumb that concludes the empirical work reported in this thesis is once again rooted in the conceptual framework detailed at the beginning of the thesis. It offered a window into necessary trade-offs, long term effects and the neural adaptations involved with significant and generalisable augmented-hand motor learning. This thesis contributes to the improvement of targeted human machine interfaces design for extra robotic limbs. The non-intrusive biosignals identified have the potential to be further explored and be applied for the control of degrees of freedom of more sophisticated robotic arms to enable more advanced augmentation. This thesis also contributes to a deeper understanding of the consequences of semi-intensive use of robotic body augmentation at behavioural and neural level.