task_9/README.md

Fast-WAM Task 9

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 purple object and put it in the black saucepan.

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 for installation and client usage, FINETUNE.md for the full adaptation workflow, and GITHUB_CODE.md for the matching code. Keep this task's weights, normalization, and prompt embedding together.

The final held-out action flow loss is above the lowest intermediate value. This is the requested continued specialist, not a validation-selected best checkpoint. Its physical performance must be measured; additional training alone does not demonstrate an improvement.