---

name: vla-adapter description: "Guides VLA-Adapter tiny-scale vision-language-action model setup, fine-tuning, evaluation, deployment, and package API workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

VLA-Adapter External-Checkout Adapter

This skill is an external-checkout adapter. Its generated directory contains only builders, validators, and documentation/references. It must not contain or vendor the native prismatic/, vla-scripts/, experiments/robot/, or other runtime trees. Those paths belong only to the separately provisioned native checkout at VLA_ADAPTER_REPO_ROOT.

Use this skill when the task involves VLA-Adapter, Prismatic VLMs, tiny-scale vision-language-action policies, LIBERO/CALVIN benchmark evaluation, ALOHA real-robot deployment, LoRA adapter fine-tuning, OpenVLA checkpoint conversion, or debugging the native checkout's workflows.

Set an absolute checkout root and install the native distribution before any native import or workflow:

export VLA_ADAPTER_REPO_ROOT=/abs/path/to/VLA-Adapter
cd "$VLA_ADAPTER_REPO_ROOT"
python -m pip install -e "$VLA_ADAPTER_REPO_ROOT"  # import package: prismatic

The checkout must provide prismatic/, vla-scripts/, experiments/robot/, scripts/, pyproject.toml, compatible pretrained VLM/config assets, and a complete local checkpoint. LIBERO, CALVIN, and ALOHA are external prerequisites: install the selected simulator/data stack, CALVIN ABC→D checkout/assets, or ALOHA TFDS/ROS/robot dependencies. A checkpoint must match the platform and include config.json, weights, and dataset_statistics.json when native VLA action unnormalization is needed.

Every native command must begin with cd <absolute-repo-root> and must run from VLA_ADAPTER_REPO_ROOT, never from this generated skill directory. The bundled builders and validators are not native entrypoints: they only render commands or inspect synthetic/local layouts. This skill is not self-contained, does not install external prerequisites, and does not execute training, evaluation, deployment, serving, robot clients, or package loading/conversion.

First checks

1. Read references/repo-provenance.md before deciding whether this skill is current for a checkout or installed package.
2. Read references/package-overview.md for the repository layout, supported workflows, dependency classes, and public model surfaces.
3. Run scripts/check_vla_adapter_env.py in the target environment to check imports, PyTorch CUDA, TensorFlow/RLDS, and optional benchmark/robot stacks without launching training or a robot.
4. If install, backend, data layout, or optional dependency errors appear, read references/troubleshooting.md and then route to the focused sub-skill below.

Minimal import smoke for a prepared package environment:

python - <<'PY'
import prismatic, torch
print("prismatic import: ok")
print("torch:", torch.__version__, "cuda:", torch.cuda.is_available())
PY

Route by task

Task shape	Read
Install, dependency selection, benchmark data/checkpoint placement, VLM config directories, LIBERO/CALVIN/ALOHA storage planning	setup-and-data
Build VLA-Adapter fine-tuning commands, choose VRAM profiles, configure LoRA/Pro/proprio/frozen options, handle W&B/log/checkpoint output	training
Run or debug LIBERO and CALVIN evaluation plans, map checkpoints to suites, interpret success logs, avoid benchmark dependency traps	evaluation
Serve a policy, validate MsgPack/JSON action payloads, run ALOHA fake-client sanity checks, adapt real ROS topics safely	deployment
Inspect the package layout/API reference and checkpoint surfaces without implementing loaders, action heads, or conversions	package-apis

All linked sub-skills preserve the same external-checkout boundary and local links; use the package API reference as a map, not as an implementation.

Operating principles

• Treat CUDA as required for real model action generation, training, benchmark rollouts, and serving. CPU checks prove importability only.
• Treat LIBERO, CALVIN, FlashAttention, ROS, robot hardware, checkpoints, and datasets as optional stacks that must be installed or pointed to explicitly.
• Do not run full training, benchmark rollouts, or real robot clients until data roots, checkpoint layouts, unnormalization keys, GPU memory, and safety boundaries are checked.
• The repository uses command-line arguments to infer robot constants (libero, calvin, aloha, or bridge). If action dimensions look wrong, verify the launched command contains the intended platform word and read package-apis/references/api-reference.md.
• Prefer the enhanced Pro checkpoints and Pro training flag unless a task asks to reproduce the original non-Pro baseline.

Common entry points

The following paths are skill-local helpers and are invoked by absolute skill path. They do not replace the native source entrypoints:

• Environment/import check: python "$VLA_ADAPTER_SKILL_ROOT/scripts/check_vla_adapter_env.py" --repo-root "$VLA_ADAPTER_REPO_ROOT"
• Data/checkpoint layout check: python "$VLA_ADAPTER_SKILL_ROOT/sub-skills/setup-and-data/scripts/validate_data_layout.py" --benchmark libero --data-root "$VLA_ADAPTER_REPO_ROOT/data/libero" --checkpoint "$VLA_ADAPTER_REPO_ROOT/outputs/LIBERO-Spatial-Pro" --vlm-config-dir "$VLA_ADAPTER_REPO_ROOT/pretrained_models/configs"
• Fine-tuning command renderer: python "$VLA_ADAPTER_SKILL_ROOT/sub-skills/training/scripts/build_finetune_command.py" --repo-root "$VLA_ADAPTER_REPO_ROOT" --help; native entrypoint is vla-scripts/finetune.py.
• Evaluation command renderer: python "$VLA_ADAPTER_SKILL_ROOT/sub-skills/evaluation/scripts/build_eval_command.py" --repo-root "$VLA_ADAPTER_REPO_ROOT" --help; native entrypoints are experiments/robot/libero/run_libero_eval.py and vla-scripts/evaluate_calvin.py.
• ALOHA command renderer: python "$VLA_ADAPTER_SKILL_ROOT/sub-skills/deployment/scripts/build_aloha_launch.py" --repo-root "$VLA_ADAPTER_REPO_ROOT" --help; native entrypoints are the server/client scripts named in deployment references.
• Synthetic MsgPack validator: python "$VLA_ADAPTER_SKILL_ROOT/sub-skills/deployment/scripts/validate_msgpack_payload.py" --help; it is not a server or client.
• Checkpoint layout validator: python "$VLA_ADAPTER_SKILL_ROOT/sub-skills/package-apis/scripts/check_checkpoint_layout.py" --help; it does not load a model.

VLA_ADAPTER_SKILL_ROOT is the installed/generated skill directory and VLA_ADAPTER_REPO_ROOT is the separate native source checkout. Never resolve native relative paths from the skill directory or copy native runtime trees into it.