Award-Winning Artificial Intelligence Research Brings the Sense of Touch to Robotics
Birmingham, Tuesday, 9 June 2026.
Professor Victor Chang’s award-winning artificial intelligence research drastically cuts energy consumption, securing new funding today to integrate this technology into physical hardware that gives robots a sense of touch.
A Trifecta of Technology Awards
On June 9, 2026, Professor Victor Chang of Aston Business School was named the winner of the “Data and Analytics Project of the Year” at the National Technology Awards in Birmingham, United Kingdom [1]. The accolade recognizes his team’s pioneering federated learning architecture, which seamlessly integrates 6G networks, healthcare intelligence, and neuromorphic edge computing [1]. By utilizing neuromorphic spiking neural networks that co-locate memory and processing, this innovative system can reduce power consumption by a factor of up to 100, meaning a legacy system requiring 100 units of energy could potentially operate on just 1 unit [1]. For the technology sector, such drastic energy efficiency presents a highly lucrative proposition for scaling artificial intelligence operations without proportionally inflating energy costs [GPT].
Bridging the Gap Between Vision and Touch
The commercial realization of this research is currently taking shape through the MultiPad consortium, a collaborative project funded by a £989,455 Innovate UK grant [1]. Led by TG0 Ltd, the initiative boasts financial and technical backing from heavyweights in the technology and robotics sectors, including Microsoft, Samsung, and Boston Dynamics [1]. Aston University’s contribution, spearheaded by Professor Chang, focuses on validating field-programmable gate array (FPGA) performance data and developing edge-deployable algorithms for a novel tactile-sensing semiconductor chip [1].
Market Implications for Edge Computing
At the core of this commercial viability is the system’s approach to data privacy and operational efficiency. The artificial intelligence models are trained locally on devices, sharing only encrypted and aggregated parameters rather than raw, sensitive information [1]. “The whole point of this work was to build something hospitals and infrastructure operators would actually use,” Professor Chang explained, emphasizing the necessity of keeping data localized and running inference directly at the edge [1]. Making the system’s reasoning transparent to its users ensures that critical infrastructure operators can trust the technology before deploying it at scale [1].