Deals · Physical AI / Robotics
Kinematic Trees raises £585K Pre-seed for robot-agnostic intelligence software
A UK robotics software company building a biology-inspired intelligence layer that makes robotics expertise portable across different hardware platforms — modular, reusable components that deploy on any manufacturer's system without being rebuilt from scratch.
“Advances in sensors, motors, and AI are now bringing robotics to people and businesses at exceptionally low cost. We're providing the intelligence layer to make those robots useful, inherently safe, and — perhaps most importantly — customisable. Robots should be able to do whatever we want them to, rather than what any particular company thinks we might want them to.”
Kinematic Trees (London) has raised £585,000 / approximately €680,000 in a Pre-seed round backed by Haatch through its SEIS Fund, the D2N2 Early-Stage Angel Investment Fund, and the British Business Bank (BBB) Syndicate Fund. No lead investor was named. The company was founded in 2025 by Dr Stuart Wilson (CEO, Senior Lecturer in Computational Neuroscience at the University of Sheffield) and Daniel Camilleri (CTO, previously co-founder of Cyberselves Universal, which became Bettering Our Worlds / BOW). The concept originated from a conversation between the two founders at Stanford University's Living Machines conference in 2017.
The software portability problem
Modern robotics faces a specific paradox. Hardware costs have fallen sharply — advances in sensors, motors, and on-device AI have made capable robotic systems accessible to manufacturers, warehouses, and even consumers at prices that were implausible five years ago. The deployment problem has not followed. A robot programmed for one vendor's platform does not transfer its capabilities to another manufacturer's hardware. Expertise built into a factory's automation system walks out the door when the engineer who configured it moves on. Production lines across Europe hold dormant robots whose original software no longer runs and no one in the building can revive.
The vendor lock-in is not primarily a pricing problem — it is a knowledge problem. Skills and behaviours encoded for one hardware system cannot be reused on a different one, which means every new robot deployment is a from-scratch engineering exercise. The cumulative cost of that redundancy — in time, in talent, and in stranded hardware — is what Kinematic Trees is pricing as its market.
Modular intelligence, biology-inspired
Kinematic Trees' response is a software integration layer that packages robotics expertise in modular, reusable components inspired by biological intelligence. The premise, grounded in Wilson's research in computational neuroscience, is that adaptive behaviour emerges from composable, transferable modules rather than hardware-specific programs — the same way animal brains learn behaviours that generalise across different physical embodiments.
"Our robot-agnostic intelligence layer provides the software foundation to make robots more useful, adaptable and safe, while allowing advanced control systems and expertise to be reused across different hardware platforms," said Wilson.
CTO Daniel Camilleri spent the years before this company working on the same hypothesis through a different vehicle. His previous company, Cyberselves Universal, became BOW (Bettering Our Worlds), which pursued a "universal" robotics software platform approach. Kinematic Trees takes the architecture further, grounding it more explicitly in cognitive science.
"The technology has been 10 years away for about 40 years," said Camilleri. "At Kinematic Trees this is the enabler: bringing robots into mass adoption needs portability of software across different robots and an ecosystem of software capabilities so robots can become personal."
The £585,000 funds team expansion across the three cities where the founding team operates — London, Nottinghamshire, and Sheffield — plus product development, international partnerships, and the first customer factory deployments targeted for H2 2026.
Sources
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