Opteran develops bio-inspired autonomy software for efficient robotic navigation and decision-making.
Opteran is a British autonomy software company developing lightweight AI for robots and autonomous systems inspired by the intelligence of insects and animals. Founded in 2019, the company reverse-engineers biological navigation and perception to create neuromorphic software that enables robots to operate efficiently without relying on large neural networks, cloud computing, or extensive training data. Its software provides real-time navigation, obstacle avoidance, and decision-making for robots, drones, and other autonomous machines while running on low-power hardware. By focusing on biologically inspired intelligence, Opteran offers an alternative approach to embodied AI that prioritizes efficiency, robustness, and autonomy in real-world environments.
In 2024, Opteran highlighted industrial and advanced robotics work with partners including SAFELOG, Airbus, ESA, and the UK Space Agency. The company framed Opteran Mind as a general-purpose neuromorphic autonomy layer for robots navigating warehouses, rough terrain, and off-world environments.
Opteran was founded in 2019 and launched in 2020 on the back of more than a decade of research into insect and animal vision, navigation, and decision-making. The company positioned itself as a biomimetic alternative to conventional AI-first autonomy stacks.
In June 2022, Opteran announced a $12 million Seed+ round led by Join Capital. The company said it had already delivered stabilized vision, collision avoidance, and navigation, and planned to use the funding to expand internationally and deepen its product stack.
In 2024, Opteran highlighted industrial and advanced robotics work with partners including SAFELOG, Airbus, ESA, and the UK Space Agency. The company framed Opteran Mind as a general-purpose neuromorphic autonomy layer for robots navigating warehouses, rough terrain, and off-world environments.
Opteran builds neuromorphic autonomy software inspired by insect brains rather than data-hungry deep learning stacks. Its product strategy is to provide general-purpose, edge-native perception and navigation for machines that need robust autonomy in dynamic environments without relying on large compute budgets.
