X Square Robot Reaches $2.8 Billion Valuation Following Series C Funding

X Square Robot has completed four consecutive financing rounds culminating in a Series C investment, bringing the embodied AI company's valuation to more than $2.8 billion. The Shenzhen-based startup said the new funding will accelerate research into foundation AI models, robotics technologies and the development of general-purpose embodied intelligence.

Founded in 2023, X Square Robot develops end-to-end embodied AI systems that combine foundation models, robotics hardware and proprietary data pipelines. Rather than relying on conventional rule-based automation, the company's platform enables robots to perceive, reason and adapt to changing environments while performing a broad range of real-world tasks.

Wang Qian, Founder and CEO of X Square Robot, stated:

"Since Day 1, X Square Robot has focused on in-house development of foundation models, pursuing a challenging but necessary path. Today, our investments in embodied AI models; a scalable, model-driven, high-quality data pipeline system; and real-world deployment are beginning to deliver clear results."

The investment attracted a combination of strategic technology companies, industrial partners and venture capital firms. IDG participated in the Series C financing, while HongShan and Xiaomi have continued supporting the company through multiple funding rounds.

Earlier this year, X Square Robot introduced WALL-B, a unified foundation model designed to integrate perception, language, reasoning and robot actions within a single neural architecture. The company has also open-sourced WALL-OSS-0.5 and WALL-WM to advance embodied AI research and improve robot manipulation, world modeling and physical reasoning.

Beyond research, X Square Robot is expanding commercial deployments across household, industrial and logistics environments. Through partnerships including 58.com and its X Family Member Program, robots are performing real-world tasks inside homes, enabling continuous data collection that strengthens AI model performance.