It introduces a memory-augmented high-level policy that leverages a world model for test-time reasoning, combined with a large-scale low-level policy trained on 40,115 hours of heterogeneous real-world robot data, and is fully open-sourced.
This repo is the official implementation of "τ0-VLA: a Hierarchical Robot Foundation Model with World-Model-Guided Test-Time Computation".
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