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Westlake Robotics Raises Over 100 Million Yuan to Enhance Dual-Pretraining Technology
Original from leaderobot.com: Westlake Robotics Secures Over 100 Million Yuan in Latest Funding Round

Westlake Robotics Raises Over 100 Million Yuan to Enhance Dual-Pretraining Technology

Westlake Robotics completed a funding round exceeding 100 million yuan in June 2026, backed by Henan Investment Group's Huirong Fund. This marks the company's third financing round in five months, with total disclosed funding reaching several hundred million yuan. The funding is aimed at advancing their unique dual-pretraining technology, which combines a general brain and a humanoid small brain for improved robotic performance.

The significance of this funding lies in Westlake Robotics' differentiated technical approach, utilizing a dual-pretraining model that integrates VLA and world model fusion. Their General Action Expert (GAE) model addresses common issues in humanoid robots, such as stiffness and imbalance, by enabling real-time interpretation of human motion intent. This capability allows a single operator to control multiple Westlake o1 robots simultaneously, reducing deployment and labor costs in applications like logistics inspection and data collection.

Looking ahead, Westlake Robotics plans to use the latest funding to further develop their unified embodied large model and accelerate deployment with industrial clients. The company, founded by academic researchers from Westlake University, is positioned to leverage its strong academic background to bridge the gap between laboratory demonstrations and real-world applications. No further timeline was disclosed at the time of publication.

Editor's Note

The influx of local state-owned capital into robotics, particularly in embodied intelligence, signals a strategic shift towards practical applications in advanced manufacturing and logistics. Westlake Robotics' approach of separating cognitive and execution tasks through dual-pretraining models may set a precedent for future developments in humanoid robotics. Observing how these models perform in real-world scenarios will be crucial for understanding their market viability.

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