| # Fast-WAM Task 4 | |
| Independent GELLO specialist initialized from the pinned Fast-WAM Base LIBERO checkpoint. This package contains a LoRA adapter and trained pose10 input/output projections; the original base model and VAE are also required. | |
| Task prompt: `pick up the bread and put it in the red bowl`. | |
| Use this folder with the repository's `inference/serve_fastwam.py`, its own `normalization.json`, and its exact `prompt.txt`. Input is the current external RGB image and measured base-frame flange pose10. Output is 32 absolute next-achieved flange pose10 targets with commanded aperture, at 15 Hz target spacing. The server predicts actions without generating future video. Target spacing does not imply 15 inference requests per second. | |
| `INFERENCE_REPORT.json` records all 40 fixed held-out windows across five episodes. The final adapter passed current-image latent parity, action normalization, finite pose10 output, orthonormal rotation, deterministic inference, HTTP query parity, and a native-versus-portable comparison on the same recorded input. These checks establish software compatibility in the recorded environment, not closed-loop physical task success. | |
| See [INFERENCE.md](../INFERENCE.md) for installation and client usage, [FINETUNE.md](../FINETUNE.md) for the full adaptation workflow, and [GITHUB_CODE.md](../GITHUB_CODE.md) for the matching code. Keep this task's weights, normalization, and prompt embedding together. | |