X Square Robot brings its valuation to $2.8B with four consecutive funding rounds

X Square Robot performing household tasks in a home environment. | today said it has closed four consecutive financing rounds, culminating in a Series C.

These rounds bring the embodied AI and foundation model developer’s valuation to more than $2.8 billion.  The Shenzhen, China-based company said it will use the funding to further invest in foundational research and core technologies. X Square Robot said it plans to advance toward general-purpose embodied AI. “Since Day 1, X Square Robot has focused on in-house development of foundation models, pursuing a challenging but necessary path,” stated Wang Qian, founder and CEO of X Square Robot. “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.” Founded in 2023, X Square Robot develops “end-to-end” embodied AI systems. Rather than rely on traditional rule-based automation, the company said its approach enables robots to adapt to changing environments and generalize across a wide range of tasks. The financing brings together strategic and financial investors, including leading technology companies, industrial partners, and venture capital firms, the company said. IDG participated in the Series C round, while HongShan and Xiaomi have backed X Square in multiple previous rounds. Submit your session idea for the 2026 RoboBusiness X Square Robot focuses on end-to-end autonomy X Square Robot said it is building a full-stack embodied AI system. Its system combines foundation models, robotics hardware, a proprietary data-pipeline system, and real-world deployments. At its core is a general-purpose embodied AI model designed to enable robots to perceive, reason, and act in complex physical environments. In April 2026, the company introduced WALL-B, a foundation model built on its World Unified Model architecture. Unlike modular vision-language-action (VLA) approaches, WALL-B trains perception, language, action, and physical prediction within a unified network. This enables stronger multimodal understanding, spatial reasoning, and continual learning from real-world interactions, according to X Square. The company has also open-sourced WALL-OSS-0.5 and WALL-WM, extending its unified approach to robot manipulation and world modeling. WALL-OSS-0.5 achieved over 80% autonomous completion on four of 17 real-robot tasks without post-training, X Square said.