Building a Foundation Stack for General-Purpose Robots
Large language models gave artificial intelligence a working recipe. Pretrain a large model on broad data, and general capability follows. 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.
Key Takeaways
- 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.
- The first is that the basic unit of robot data is an interaction, not a trajectory; a demonstration is successful only if it changes the world as intended, not simply because the joints moved.
The second is that pretraining should yield usable capability, not just an initialization for later fine-tuning.
- Robot learning data: Engineering for quality and cost, not scale For the X Square Robot team, one of the biggest constraints on general-purpose robots is the cost and quality of interaction data, not the number of parameters.
To address that, the company built its Universal Manipulation Interface (UMI) data collection system, QUANXTA Zero Series .
- X Square Robot The first is quality control, and it is the most distinctive part.
Rather than accepting recorded trajectories as they are, the system runs a closed inspection loop, and its notable step is physical playback.
- The company pretrains on a large volume of robot-free demonstrations to build general representations, then adds a small amount of real-robot data as an anchor to the specific machine's dynamics.
Large language models gave artificial intelligence a working recipe. Pretrain a large model on broad data, and general capability follows.
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 shares its vision of bringing robots into real homes.
For more details please read the original article at IEEE Spectrum AI.
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