| # policy-checkpoints/ |
|
|
| Final checkpoints of the Appendix-D flow-matching policy (12.6M params: |
| ResNet-18 visual stem + proprio stem + 4-block transformer encoder + 4-block |
| flow-matching action decoder, horizon 16), trained on 585 generated |
| observation/action samples from 5 TACO sequences (5 train / 1 held-out val). |
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| Files: |
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| - `best.pt`, `last.pt` — `torch.save` dicts with keys `model`, `config`, |
| `stats` (NO optimizer states). `best.pt` = best val-loss step (~250). |
| - `best_ts.pt` — TorchScript export of `sample_action` (B=1, normalizer |
| buffers baked in), traced on CUDA; loadable in the Isaac Sim venv without |
| timm (`torch.jit.load`). Parity vs eager: 6e-8. |
| - `stats.npz`, `dataset_meta.json` — dataset normalization stats and metadata |
| required to run the policy. |
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|
| Produced by (repo https://github.com/physercoe/egoengine-repro, see |
| `docs/POLICY.md`): |
|
|
| ``` |
| .venv-policy/bin/python scripts/train_policy.py # bounded run |
| .venv-policy/bin/python scripts/export_policy.py # -> best_ts.pt |
| ``` |
|
|
| Bounded closed-loop sim eval (`scripts/eval_policy.py`, 5 rollouts/seq): |
| train seq 4/5 (0.80), held-out seq 0/5 (0.00) — a pipeline proof at 585 |
| samples, NOT a policy-quality result (thin-data caveat, `docs/POLICY.md` §2). |
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| License: MIT (our weights/code). TACO-derived training data: CC BY 4.0. |
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