README.md

---
license: cc-by-4.0
language:
- en
size_categories:
- n<1K
pretty_name: Robot Execution Validation
tags:
- robotics
- vision-language-action
- robot-manipulation
- evaluation
- reproducibility
---
# Robot Execution Validation

VLABench execution records for released SmolVLA and Pi0 policies: 320 primary
confirmation attempts, a prospectively specified 60-attempt family-matched native
validation extension, and 37 separately reported development attempts. Each native
trajectory is evaluated at 60 and 200 actions. Paired instructions use the same
initial state and requested-versus-alternative goal predicates.

| File | Contents |
|---|---|
| `confirmation.json` | Frozen primary results, per-trial measurements, summaries, and raw-file hashes |
| `extension.json` | Frozen validation-extension results and the prospective configuration selection |
| `reproduction.zip` | Evaluation/analysis code, tests, protocols, selected inputs, complete action traces, initial states, scene images, and environment versions |
| `SHA256SUMS.json` | File checksums |

## Reproduce the analysis

Download the files from one fixed repository revision and extract `reproduction.zip`.
The archive has its own file-level `SHA256SUMS.json`. Python 3.10 or later with NumPy,
SciPy, Matplotlib, and pytest is sufficient for analysis; no GPU is required.
Use the recorded package versions for exact regeneration.

From the extracted directory:

```bash
cp /path/to/download/confirmation.json code/paper/reproduce/revision_results.json
cp /path/to/download/extension.json code/paper/reproduce/validation_extension_results.json
cd code
export PYTHONPATH="$PWD/src:$PWD"
python -m pytest tests -q
python -m experiments.revision.freeze --root ../confirmation --out ../confirmation_check.json
python -m experiments.revision.freeze_extension --root ../extension --out ../extension_check.json
python -m paper.reproduce.revision_assets --out ../latex_assets
python -m paper.reproduce.extension_assets --out ../latex_assets
```

The checked exports reproduce the downloaded JSON files. The generated assets include
the original four-family native results, paired outcomes, progress, development results,
family-matched extension and synthesis, and stopping-rule sensitivity. Technical errors
remain distinct from observed goal outcomes. The paired first-goal analysis is post hoc;
the original native-stop outcomes are retained alongside it.

Extension validation compares the frozen selected configurations with the recorded inputs,
undoing only VLABench's automatic expansion of relative XML asset paths. No raw record or
prospective selection is changed. The extension retains three Pi0 numerical failures.

## Rerun execution

Use the package inventories in `environments/versions.json` and install these pinned
upstream sources and their assets in separate simulator and policy environments:

- [VLABench](https://github.com/OpenMOSS/VLABench), revision `cf588fe60c0c7282174fe979f5913170cfe69017`.
- [SmolVLA checkpoint](https://huggingface.co/lerobot/smolvla_vlabench), revision `4fd586e12dc14b04d9d606ddbb77448df4f0ff29`.
- [Pi0 checkpoint](https://huggingface.co/VLABench/pi0-primitive-10task), revision `1ad73753a74d5cd97e67856664350f3f0baa21dc`.
- [Author OpenPI implementation](https://github.com/Shiduo-zh/openpi), revision `4483d1da6332da44115fe530e4e6fdd89bd57b13`; use the included `openpi_uv.lock`.

Initialize a local Git checkout of `code/` before execution so the evaluator can
record the reproduction's source revision. From `code/`, relocate archived asset
references into a fresh directory:

```bash
python -m experiments.revision.prepare_reproduction --inputs ../inputs \
  --vlabench-root /path/to/VLABench/VLABench --out ../relocated
```

Set `PROJECT_STORAGE_ROOT` to the absolute path of `../relocated`, `VLABENCH_ROOT`
to the installed benchmark package, and `PYTHONPATH` to `code/src:code` using absolute
paths. Set `MUJOCO_GL=egl`, `OMP_NUM_THREADS=4`, and `OPENBLAS_NUM_THREADS=4`.
The evaluator permits physical GPUs 0 through 4 and enforces one renderer at a time.
Keep inference and rendering on separate GPUs.

Start the services in their respective policy environments:

```bash
python -m experiments.e2a.smolvla_server --gpu 1 --port 5581
python -m experiments.revision.pi0_server --gpu 3 --port 5583 \
  --repo /path/to/openpi --checkpoint /path/to/pi0-checkpoint
```

Run the evaluator in the simulator environment with `CUDA_VISIBLE_DEVICES=0`.
For the extension, use `--tasks select_book,select_mahjong,select_poker` and the
relocated published track. For the original native control, use
`--tasks select_fruit,select_toy,select_chemistry_tube,select_drink`.

```bash
python -m experiments.revision.vlabench_control --out /path/to/fresh/run \
  --tasks select_book,select_mahjong,select_poker \
  --track-config ../relocated/track_1_in_distribution.json --episodes-per-task 10 \
  --policy-seed 17 --max-substeps 1 --max-steps 200 --port 5581 --chunk 50
```

Use `--port 5583 --chunk 5` for Pi0. For paired instructions, replace `--tasks` and
`--track-config` with `--paired-spec ../relocated/final_study_command_spec.json`.
Use fresh output directories and compare actual initial-state and observation hashes.
The common 200-action diagnostic budget is not every task's official leaderboard budget.

The separate synthetic-speech records are available in
[multilingual-robot-grounding-causal-audit](https://huggingface.co/datasets/nur-dev/multilingual-robot-grounding-causal-audit/tree/0d82c875c6d58b1406b0217c9fc5deb0904df0f2).