Robotic extra fingers and arms are being designed to give able-bodied people new motor abilities, but no one knows how the brain should control them without interfering with the person’s own limbs. This matters because motor augmentation technology—robotic devices that add functions rather than replacing lost ones—is advancing faster than the neuroscience to support it. The core gap is cognitive and physiological: how do you send commands to an extra robotic limb and receive touch feedback from it, without overwhelming the brain’s existing control of your biological arms and hands? The AUGMENT project will first identify which neurocognitive mechanisms are best suited for controlling these devices, then test how to integrate somatosensory feedback so that learning to use an extra limb feels intuitive and transfers naturally to other tasks. If successful, the research could transform assistive technology for disabled individuals, moving beyond simple substitution of lost functions toward genuine motor augmentation—giving someone with limited mobility, for example, a robotic arm that extends their reach or grip in ways their biological body cannot. The work is fundamentally curiosity-driven, rooted in basic sensorimotor science, but its solutions for the human-device interface will directly shape how future robotic augmentation is designed, controlled, and adopted in daily life.
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AUGMENT is centred around the unique challenge of controlling motor augmentation technology. We are witnessing the rise of a novel class of technologies, designed to resemble human limbs in their functionalities. These extra fingers and arms are robotic devices that are designed to extend the user's motor capabilities (hereafter X-devices). Beyond the traditional substitution of missing functions, X-devices can be further exploited to provide additional motor functions to already fully functional individuals. But what cognitive and physiological resources could we utilise to control extra limbs, in addition to our own? The goal of AUGMENT is to harness basic understanding of human motor learning and control to guide successful technological development of X-devices in abled and disabled individuals. An urgent question is how to provide motor commands and somatosensory feedback to and from the X-device without restricting the cognitive and motor control of the biological limbs. I will address this by first identifying the neurocognitive mechanisms best suited for successful X-devices motor control. Then I will investigate the optimal integration of somatosensory information from the X-device to afford intuitive and transferable motor skill learning. Finally, I will provide innovative solutions for increasing the functionality of disabled individuals in daily life, beyond traditional substitution, using novel X-devices. By identifying and solving the fundamental sensorimotor human-device interface challenges across diverse user groups and functional needs, AUGMENT will be crucial for the realisation of motor augmentation and other related technologies that aim to put the user at the centre of robot control, design and development.
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