AirVLA FT-DAG (pi0, step 17500, on-policy corrective)
DAgger-style on-policy corrective fine-tune: expert completions recorded from states the policy itself reaches. Improves terminal precision but does not unlock the grasp, and significantly degrades target selection.
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_v24 (FT-C list + 160 on-policy corrective episodes) |
| Initialised from | FT-C |
| Training steps | 20,000 (lr 5e-6 -> 5e-7); checkpoint 17,500 selected |
| Checkpoint selection | lowest pinned-noise validation MSE over the saved ladder |
Frozen-protocol result (n = 60 pick + 20 navigation, paired scenes)
1/60 grasped - 37/60 correct-target - 196.0 mm median - 82.0 mm target-true, the best pure-policy precision of the campaign - 12/20 navigation. The selection regression vs FT-C is significant (exact McNemar p = .013).
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.
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