Learning Robot Behavior Priors from Non-Robotic Synthetic Data

The paper explores whether inexpensive, procedurally generated synthetic data can provide useful priors for robot learning models before any real-world data is observed. By creating three data generation pipelines that capture mathematical abstractions of robotic control, the authors pretrain a Transformer and evaluate its zero‑shot action prediction on unseen physical robot tasks. The results show that the model achieves non‑trivial performance via in‑context learning, with specialized control priors outperforming generic causal priors, though a compute‑matched real‑robot baseline still excels on its own training datasets.

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• llms
• robotics