$τ_0$-VLA: a Hierarchical Robot Foundation Model with World-Model-Guided Test-Time Computation

Key points

The high-level policy looks at the current observation, the full instruction, previous small tasks, and execution history together to propose the next task.
In the extra compute stage, it expands candidates over several steps, keeps only the good ones using the predicted final scene and cumulative score, and then regenerates the final task.
Four long-horizon tasks were evaluated with 10 real robot trials per task. The mean success rate of direct execution with the same low-level policy rose from 27.5 percent to 45.0 percent in hierarchical execution without retrieval.
With extra compute turned on, the number of successful trials across three tasks increased from 5 to 7 for making tea, from 6 to 9 for tidying books, and from 5 to 7 for cleaning a room.
On predicting the next task for unseen book arrangements, accuracy was 50.0 percent for a single prediction, 57.5 percent for one-step candidate comparison, and 74.0 percent for multi-step extra compute. However, task-specific fine-tuning and baseline adjustment were included.

External research summaries. These are not HDATF publications or measured product results.