Neuromorphic Texture: From Human to Machine

The human sense of touch is paramount to the way in which we interact with the world around us. The identification of textures is an important aspect of our active sensing, our approach to manipulation tasks and our ability to avoid potentially noxious surfaces1. The number of people who experience upper limb loss is predicted to increase to 3.6 million in the US alone by 20502, creating a more urgent need for tactile prosthesis that are able to relay information on their status back to the user. Likewise, in order for robotic manipulators to be able to perform complex tasks in a more dynamic and precise manner, they must be able to effectively sense and process their environment via touch.

In order to address these issues, it's often natural to look to nature for answers. Tactile stimuli are processed within biological systems as trains of spikes transmitted between neurons. Neuromorphic systems seek to emulate the spiking behaviour of biological neurons and introduce potential improvements in computing speed and power efficiency when processing data3. A shift from traditional deep neural networks (DNNs) to spiking neural networks (SNNs) has been previously encouraged for these reasons4. Research in this area also allows us to apply and test theories derived from other disciplines such as neuroscience and biology, within the context or touch. Biologically inspired learning rules, such as Spike timing dependent plasticity (STDP)5, different neuronal models6,7, and the pre-processing of data in afferents8 are some of such theories that will be explored through research and development around neuromorphic touch.

Although texture classification has been explored previously9, an end-to-end solution utilising entirely neuromorphic hardware and processing techniques remains novel. Through the development of this, we seek to investigate the true benefits of neuromorphic solutions as they apply to speed and power efficiency.

FARSCOPE-TU will deliver a step change in UK capabilities in robotics and autonomous systems (RAS) by elevating technologies from niche to ubiquity. It meets the critical need for advanced RAS, placing the UK in prime position to capture a significant proportion of the estimated $18bn global market in advanced service robotics. FARSCOPE-TU will provide an advanced training network in RAS, pump priming a generation of professional and adaptable engineers and leaders who can integrate fundamental and applied innovation, thereby making impact across all the "four nations" in EPSRC's Delivery Plan. Specifically, it will have significant immediate and ongoing impact in the following six areas:
1. Training: The FARSCOPE-TU coherent strategy will deliver five cohorts trained in state-of-the-art RAS research, enterprise, responsible innovation and communication. Our students will be trained with wide knowledge of all robotics, and deep specialist skills in core domains, all within the context of the 'innovation pipeline', meeting the need for 'can-do' research engineers, unafraid to tackle new and emergent technical challenges. Students will graduate as future thought leaders, ready for deployment across UK research and industrial innovation.
2. Partner and industrial impact: The FARSCOPE-TU programme has been designed in collaboration with our industrial and end-user partners, including: DSTL; Thales; Atkins; Toshiba; Roke Manor Research; Network Rail; BT; National Nuclear Lab; AECOM; RNTNE Hospital; Designability; Bristol Heart Inst.; FiveAI; Ordnance Survey; TVS; Shadow Robot Co.; React AI; RACE (part of UKAEA) and Aimsun. Partners will deliver context and application-oriented training direct to the students throughout the course, ensuring graduates are perfectly placed to transition into their businesses and deliver rapid impact.
3. RAS community: FARSCOPE-TU will act as multidisciplinary centre in robotics and autonomous systems for the whole RAS community, provide an inclusive model for future research and training centres and bring new opportunities for networking between other centres. These include joint annual conference with other RAS CDTs and training exchanges. FARSCOPE-TU will generate significant international exposure within and beyond the RAS community, including major robotics events such as ICRA and IROS, and will interface directly with the UK-RAS network.
4. Societal Impact: FARSCOPE-TU will promote an informed debate on the adoption of autonomous robotics in society, cutting through hype and fear while promoting the highest levels of ethics and safety. All students will design and deliver public engagement events to schools and the public, generating knock-on impact in two ways: greater STEM uptake enhances future economic potential, and greater awareness makes people better users of robots, amplifying societal benefits.
5. Economic impact: FARSCOPE-TU will not only train cohorts in fundamental and applied research but will also demonstrate how to bridge the "technology valley of death" between lower and higher TRL. This will enable students to exploit their ideas in technology incubators (incl. BRL incubator, SetSquared and EngineShed) and through IP protection. FARSCOPE-TU's vision of ubiquitous robotics will extend its impact across all UK industrial and social sectors, from energy suppliers, transport and agriculture to healthcare, aging and human-machine interaction. It will pump-prime ubiquitous UK robotics, inspiring and enabling myriad new businesses and economic and social impact opportunities.
6. Long-term Impact: FARSCOPE-TU will have long-term impact beyond the funded lifetime of the Centre through a network for alumni, enabling knowledge exchange and networking between current and past students, and with partners and research groups. FARSCOPE-TU will have significant positive impact on the 80-strong non-CDT postgraduate student body in BRL, extending best-practice in supervision and training.