French startup Bleu Robotics secures €5 million to teach humanoid robots new tasks with fewer demonstrations
Bleu Robotics, a Paris-based startup building AI models for industrial humanoid robots, has raised a €5 million pre-Seed round, to reach its goal of reducing the full humanoid robot deployment process to one hour or shorter. The round was co-led by Contrarian Ventures and OVNI Capital, with participation from Kvanted, WEPA Ventures, Motier Ventures and […] The post French startup Bleu Robotics…
Paris-based AI startup Bleu Robotics has secured €5 million in a pre-Seed round to develop technology that enables industrial humanoid robots to learn new tasks with fewer demonstrations. The round was led by Contrarian Ventures and OVNI Capital, with support from Kvanted, WEPA Ventures, Motier Ventures, and Charlie Songhurst, among others.
Benoît Berkoukchi, co-founder of Bleu Robotics, explains that while most robotics companies train humanoids using millions of data points, this approach is impractical for manufacturing due to limited factory-specific data and lack of internet-scale data. Bleu Robotics' methodology, which leverages abductive learning, allows robots to learn new tasks from as few as ten operator demonstrations.
Their software runs on any humanoid platform and includes the necessary AI model and training devices to facilitate robots' deployment in factories. The company's founders, who include two INRIA researchers and a former Renault manufacturing manager, believe their approach can scale to put robots to work in factories within months rather than years.
The investment comes amid continued European interest in robotics software, humanoid systems, and physical AI. Other companies that secured funding in 2026 include Inbolt (€11 million), Trener Robotics (€26 million), Motion (€1.7 million), THEKER (€73 million), and Humanoid (€133 million), bringing the total to €372.7 million, with Bleu Robotics' latest round bringing the total to €377.7 million.
Written by urgent.news from EU-Startups's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.