# Reproduction Guide (Main LIBERO Table)

This guide targets reproduction of the paper's main LIBERO result table:

- LIBERO-Spatial
- LIBERO-Object
- LIBERO-Goal
- LIBERO-Long
- Average across the four suites

## 1) Scope and expected output

- Reproduce EVOLVE-VLA numbers for the main table (not all ablations).
- Log per-suite success and aggregated average.
- Record run metadata for repeatability.

## 2) Required environments

- `evolve-vla` conda env for training/eval.
- `vlac` conda env for reward service.

See `INSTALLATION.md` for full setup.

## 3) Required environment variables

Set these before running training scripts:

```bash
export EVOLVE_SFT_CHECKPOINT=/path/to/sft/checkpoint
export EVOLVE_OUTPUT_DIR=/path/to/output/checkpoints
export EVOLVE_ALIGN_JSON=/path/to/align.json
export EVOLVE_REWARD_BACKEND=vlac
```

Set VLAC checkpoint path before launching service:

```bash
export VLAC_CKPT_PATH=/path/to/EVOLVE-VLA/checkpoints/VLAC
```

Reference-frame roots for LIBERO suites (if required by rollout config):

```bash
export EVOLVE_LIBERO_REF_LIBERO_10=/path/to/libero_10/reference_frames
export EVOLVE_LIBERO_REF_LIBERO_OBJECT=/path/to/libero_object/reference_frames
export EVOLVE_LIBERO_REF_LIBERO_OBJECT_WRIST=/path/to/libero_object_wrist/reference_frames
export EVOLVE_LIBERO_REF_LIBERO_SPATIAL=/path/to/libero_spatial/reference_frames
export EVOLVE_LIBERO_REF_LIBERO_SPATIAL_WRIST=/path/to/libero_spatial_wrist/reference_frames
export EVOLVE_LIBERO_REF_LIBERO_GOAL=/path/to/libero_goal/reference_frames
export EVOLVE_LIBERO_REF_LIBERO_GOAL_WRIST=/path/to/libero_goal_wrist/reference_frames
```

Notes:

- Non-wrist runs only require non-wrist roots (`...LIBERO_10`, `...OBJECT`, `...SPATIAL`, `...GOAL`).
- Wrist roots are required only when using wrist-mode workers (`interval-wrist` / `interval-wrist-only`).
- For VLAC, reference frames are strongly recommended for performance.

Optional:

```bash
export WANDB_API_KEY=...
export EVOLVE_RAY_ADDRESS=ray://127.0.0.1:10001
export EVOLVE_NCCL_SOCKET_IFNAME=eth0
```

## 4) Start reward service

```bash
conda activate vlac
cd /path/to/EVOLVE-VLA/Release
python reward_model/launch_vlac_servers.py --base-port 8111
```

Health-check example:

```bash
python scripts/check_vlac_services.py --urls http://127.0.0.1:8111,http://127.0.0.1:8112
```

## 5) Run training/eval entry scripts

From `Release/`:

```bash
conda activate evolve-vla

# LIBERO-Long
python scripts/train_libero_10-sft_full-ttt.py

# 1-shot setting script (often used for spatial-oriented experiments)
python scripts/train_libero_10-sft_1shot-ttt.py

# Zero-shot transfer path
python scripts/train_libero_object-0shot-ttt.py

# Optional wrist-view finetune variant
python scripts/finetune_libero_object-0shot-ttt_with_wrist.py
```

If you are assembling strict main-table runs, align each suite's script/config with paper settings and record them in the log template below.

## 5.1) Reference-frame preparation (recommended for VLAC)

Reference frames are generated from expert demos and materially improve VLAC quality.

Fast path (one command, all suites):

```bash
python scripts/prepare_reference_frames_pipeline.py \
  --raw-datasets-root /path/to/libero/raw \
  --working-root /path/to/libero/processed \
  --reference-root /path/to/libero/reference_frames \
  --include-wrist \
  --overwrite
```

Use `--skip-regen` if you want to use downloaded low-resolution datasets directly.

Detailed workflow (release-contained scripts):

1. **Optional but recommended: regenerate LIBERO demos at higher resolution**
   - Script: `scripts/regenerate_libero_dataset.py`
   - Purpose: generate cleaner 256x256 trajectories (community versions may be low-resolution).
   - If skipped, low-resolution source can still be used with expected performance drop.
   - Example:
     ```bash
     python scripts/regenerate_libero_dataset.py \
       --libero_task_suite libero_10 \
       --libero_raw_data_dir /path/to/libero_10_raw \
       --libero_target_dir /path/to/libero_10_regen
     ```

2. **Select/export expert demos to reference-frame folders**
   - Script: `scripts/prepare_expert_demo.py`
   - Behavior: picks shortest successful demo and exports per-frame PNGs.
   - Default is agent view (`--frame_key agentview_rgb`).
   - For wrist-view references, use `--frame_key eye_in_hand_rgb`.
   - Example (agent view):
     ```bash
     python scripts/prepare_expert_demo.py \
       --libero_task_suite libero_10 \
       --libero_raw_data_dir /path/to/libero_10_regen \
       --output_dir /path/to/reference_frames/agentview \
       --overwrite
     ```
   - Example (wrist view):
     ```bash
     python scripts/prepare_expert_demo.py \
       --libero_task_suite libero_10 \
       --libero_raw_data_dir /path/to/libero_10_regen \
       --output_dir /path/to/reference_frames/wrist \
       --frame_key eye_in_hand_rgb \
       --overwrite
     ```

3. **Set exported directories as `EVOLVE_LIBERO_REF_*` roots**
   - Set non-wrist roots for standard settings.
   - Set wrist roots when running wrist-view worker modes.

## 6) Logging template (required)

For each run, record:

- date/time
- git commit hash (if repo initialized)
- script name
- backend (`vlac` by default)
- suite name
- seed
- checkpoint path
- success rate

Summary table template:

| Suite | Seed | Success (%) | Notes |
|---|---:|---:|---|
| Spatial |  |  |  |
| Object |  |  |  |
| Goal |  |  |  |
| Long |  |  |  |

Average:

```text
Avg = (Spatial + Object + Goal + Long) / 4
```

## 7) Tolerance reporting

After running multiple seeds, report:

- mean and std per suite
- mean and std for average score
- absolute difference vs paper target

Use project-agreed tolerance (for example, +/- X percentage points) once baseline variance is measured.

## 8) Temporary cross-machine validation plan

For internal transfer and bring-up validation on another server, use:

- `docs/TEMP_OTHER_MACHINE_TEST_PLAN.md`

This is a temporary internal checklist file and can be removed after validation/reproduction sign-off.