X Square Robot Unveils New Embodied AI Model, Says Robots Will Arrive in Homes in 35 Days
Backed by Alibaba, ByteDance, Xiaomi and Meituan, X Square Robot unveiled a next-generation embodied AI foundation model for home robots and said its first deployments in everyday households will begin within 35 days.
BEIJING, April 23, 2026 /PRNewswire/ -- X Square Robot on Tuesday unveiled Wall-B, a new embodied AI foundation model designed for deployment in real-world homes, marking what the company described as a major step toward bringing general-purpose robots into daily family life.
At a launch event themed "Born to Bot, Bot to Family," the company also introduced its World Unified Model (WUM) architecture, a training framework that combines vision, language, action and physical prediction within a single system from the outset. X Square said the model is intended to help robots operate in the far more unpredictable setting of a home, where tasks, layouts and interactions vary from moment to moment.
"Robots in factories and robots in homes are fundamentally different," said Qian Wang, founder and CEO of X Square Robot. "In factories, they repeat the same action 10,000 times. In a home, they may need to perform 10,000 different actions, each in a different context. The real challenge is not repetition, but whether a robot can execute new, untrained actions in an unstructured environment."
Wall-B is the company's first full implementation of its World Unified Model architecture. Unlike modular systems that train perception, language and control separately, X Square Robot said World Unified Model optimizes those capabilities jointly from the very beginning. The company said that allows physical prediction — including force, friction and collision dynamics — to emerge as part of the model itself, rather than being layered on afterward.
"We train vision, language, action and prediction in the same network from day one," said Wang Hao, chief technology officer of X Square. "Human infants do not learn to see, move and communicate in isolated stages. They learn by integrating perception and action at the same time, with constant feedback from the physical world. That is the principle behind our architecture."
X Square Robot said the model was built on two core foundations. The first is a data strategy centered on real, non-staged home environments, aimed at exposing the system to the long tail of household scenarios — misplaced objects, temporary occlusion, unexpected obstacles and spontaneous human activity. The second is a physics-aware predictive mechanism that enables the robot to anticipate physical outcomes before taking action, rather than merely reacting after contact occurs.
Together, those elements are meant to narrow one of robotics' hardest gaps: moving from controlled demos to reliable performance in live environments. The company said its work on physical robotic platforms has helped it accumulate practical experience in bridging simulation and reality across diverse operating conditions.
At the event, X Square demonstrated a series of live tasks. In one experience zone, a robot arranged flowers while adjusting its grip and motion in real time as stems shifted position under visual occlusion. The task was completed without pre-set trajectories, according to the company, and drew attention from both domestic and international media attending the event.
Even so, X Square acknowledged that the technology remains early. Wang said current systems can make mistakes that require remote intervention — such as placing slippers in the kitchen or pausing mid-task to process the next action. But he said the robots' ability to operate continuously and generate new real-world data around the clock gives the system a path to rapid improvement.
That learning loop is central to the company's next milestone: within 35 days, X Square plans to place its robots into everyday homes, underscoring the company's long-term commitment to the home robotics sector.
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