Closing the Loop in Humanoid VLA: Persistent 3D Object Tokens for Verifiable Loco-Manipulation

Key points

It points out that robots fail on long tasks not because they cannot understand instructions, but because the object state used for action differs from the object state used for verification.
POT binds objects to role slots such as target, destination, support surface, and handoff partner, and refreshes the 3D position grounding after each action chunk.
Object records are turned into fixed-format tokens containing 33 features per slot across 8 slots and inserted into the input sequence of the whole-body action model.
The same record is used to verify grasp, placement, and release through geometry-based checks, and if it fails, the system triggers re-observation, retry, and replanning.
On eight task groups with a real Unitree G1, it improves the same-condition GR00T-N1.7 baseline from 39/80 to 71/80, and cup stacking jumps from 1/10 to 8/10.
In ablation, tokens alone improve from 15/40 to 31/40, verification alone gives 22/40, and using both gives 34/40, showing that token conditioning is the main driver of performance.

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