Imitation Learning Without Pre-training Is Underrated
ihorbeaver · x · 2026-07-13
The author argues that pure imitation learning without robot pre-training is an overlooked direction that doesn't even have a unified name yet.
The reasoning is that when done well, these methods behave almost like programming: reliable, predictable, easy to modify, and easy to debug. The only difference is that the "source of truth" shifts from code to a small dataset, and the system is connected by a vision encoder and a Transformer rather than a compiled program.
He draws an analogy: Claude Code couldn't exist without years of high-quality source code accumulated from real-world use; the same goes for robotics models. Finally, he gives a robustness example: a model trained on orange zip ties performed stably under conditions with no lighting changes.
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