Building a Foundation Stack for General-Purpose Robots
IEEE Spectrum AI X Square Robot
X Square Robot, a Chinese robotics company, has developed an integrated foundation stack for general-purpose robots consisting of data collection, a world model (WALL-WM), and an action model (Wall-OSS-0.5) designed to work together as interdependent layers. The company reports achieving performance comparable to all-robot datasets at roughly 20-fold lower collection cost by combining robot-free demonstrations captured with a wearable rig with small amounts of real-robot data. The approach emphasizes data quality through physical playback validation, event-based world modeling rather than fixed-length predictions, and semantic action tokenization that transfers across different robots without retuning.
Why it matters
This article is brought to you by X Square Robot.Large language models gave artificial intelligence a working recipe. Pretrain a large model on broad data, and general capability follows. Robotics has no such recipe. Robotics systems have long been assembled from separate perception, planning, and control parts that rarely add up to intelligence a robot can carry from one task to another, or one machine to another. The central problem in embodied AI is to find the equivalent recipe, and the field does not yet agree on what it is.X Square Robot, a Chinese embodied-AI company, has made an unusually explicit bet. It argues that the recipe is an integrated stack, spanning the data a robot learns from, a world model for predicting changes in the physical world, and an action model that brings together perception, planning, reasoning, and decision-making to generate executable robot behavior. The company also believes that the stack should be built and released in the open. X Square Robot sh
