AirVLA FT-C (pi0, step 15000)
The strongest pure VLA policy of the campaign, and the policy used by both terminal-control hybrids.
Part of the AirVLA MSc dissertation campaign: adapting a pretrained pi0 vision-language-action policy to a quadcopter with a 2-DoF arm and parallel gripper, evaluated in MuJoCo 3.3.4.
| Property | Value |
|---|---|
| Base model | lerobot/pi0 |
| Training data | hanapasta/airvla_v21 (v2 + F1 terminal-corrective, F1 up-weighted) |
| Initialised from | lerobot/pi0 base |
| Training steps | 15,000 |
| Checkpoint selection | lowest pinned-noise validation MSE over the saved ladder |
Frozen-protocol result (n = 60 pick + 20 navigation, paired scenes)
1/60 grasped - 47/60 correct-target approaches - 145.8 mm median miss (87.6 mm target-true) - 12/20 navigation.
Provenance note: this is a deterministic retrain of the original FT-C checkpoint, which was lost to a storage-cleanup error. It was accepted under equivalence gates: pinned-noise validation MSE 7.7e-5 vs the original 7.679e-5 (0.3%), plus a matching closed-loop evaluation. See the ledger for the full incident record.
Usage
Evaluated with the frozen harness eval_v2.py from the code repository:
python eval_v2.py <this_checkpoint> 60 20 --torchseed 1000 --tag run --video
Code, reproduction guide and the full experimental ledger: https://github.com/robotics-hana/drone-version2
Evaluation uses MuJoCo 3.3.4; a different simulator version changes contact behaviour enough to invalidate comparison with the banked results.
- Downloads last month
- 13