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).

Files:

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.

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).

License: MIT (our weights/code). TACO-derived training data: CC BY 4.0.