GoKawiilRobotics industry grapples with scarce training data as AI scaling model falters
AI language models have advanced largely by training on ever-larger text datasets, with dataset sizes reportedly doubling roughly every six months since the early 2010s, according to Epoch AI. But researchers note there is no equivalent large public corpus of robot training data, unlike the estimated 300 trillion tokens of usable text available for language models. This gap has made sourcing and defining robot training data a central open problem for the field.
GoKawiil's interpretation of the reporting above, not reported fact.
If robotics lacks a text-like data reservoir, progress may depend on new methods such as simulation, teleoperation, or video capture rather than simply scaling existing datasets, which could slow advances compared to language models. The comparison suggests that lessons from LLM scaling may not transfer directly to embodied AI, raising questions about how quickly capable general-purpose robots can be developed.
- AI language model progress has been driven by rapidly growing training datasets, doubling roughly every six months.
- Unlike text, there is no large existing public dataset for robot training data.
- Determining how to source sufficient robot training data is described as a key unsolved problem in robotics research.
Source: construction-physics.com — Brian Potter, 2026-10-15
Published there as: “How to Train Your Robot”
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