VLA/scripts

Scripts

This directory contains the public Wall-X command-line helpers. Run the examples below from the repository root, using python scripts/... and bash scripts/.... Pass file and directory paths explicitly.

Inference smoke test

Use fake_inference.py to verify that a checkpoint can be loaded and can produce one action chunk from a synthetic LIBERO-style observation.

python scripts/fake_inference.py --checkpoint-path /path/to/checkpoint

If the training config is not stored next to the checkpoint as config.yml or config.yaml, pass it explicitly:

python scripts/fake_inference.py \
  --checkpoint-path /path/to/checkpoint \
  --train-config-path /path/to/config.yml

LIBERO evaluation

run_libero.sh is a small shell wrapper around infer_libero.py. It requires the optional LIBERO simulator stack:

pip install -r requirements-libero.txt
mkdir -p third_party
git clone https://github.com/Lifelong-Robot-Learning/LIBERO.git third_party/LIBERO

The launcher checks for LIBERO, robosuite, MuJoCo, PyOpenGL, BDDL, Gym, and h5py before loading the model. If LIBERO is cloned elsewhere, pass LIBERO_PATH=/path/to/LIBERO.

bash scripts/run_libero.sh /path/to/checkpoint

Useful environment variables:

CHECKPOINT_PATH=/path/to/checkpoint
TRAIN_CONFIG_PATH=/path/to/config.yml
TASK_SUITE_NAME=libero_spatial
TASK_INDICES=0,1,2
NUM_TRIALS_PER_TASK=50
CUDA_ID=0
SMOKE=1
MAX_INFER_TIMES=52

MAX_INFER_TIMES is optional. When omitted, the launcher uses suite-specific defaults aligned with the LIBERO evaluator: spatial 22, object 28, goal 30, libero_10 52, and libero_90 40 action chunks.

For full control, call the Python entry directly:

python scripts/infer_libero.py \
  --checkpoint-path /path/to/checkpoint \
  --task-suite-name libero_spatial \
  --num-trials-per-task 50 \
  --driver-mode in_process

You can also pass a complete eval config:

python scripts/infer_libero.py --config /path/to/eval_config.yml

WebSocket serving

run_serving.sh launches the Wall-X WebSocket server through the public vendored serving runtime. Pass paths explicitly; the script has no built-in checkpoint path.

bash scripts/run_serving.sh \
  --checkpoint-path /path/to/checkpoint \
  --train-config-path /path/to/config.yml \
  --port 32195

By default the script returns raw model action chunks, which is the expected mode for open-loop plotting. Pass --serialize-actions when your client expects robot-serialized actions.

Useful options:

CUDA_ID=0
ACTION_HORIZON=32
IMAGE_PASSING_MODE=base64
MAX_BATCH_SIZE=1

Additional launch_serving.py arguments can be forwarded after --:

bash scripts/run_serving.sh --checkpoint-path /path/to/checkpoint -- \
  --model-config.norm-key libero_all

Open-loop WebSocket evaluation

draw_openloop_plot.py compares predicted action chunks from a running WebSocket server against LeRobot dataset ground truth. --dataset-root and --train-config are required and have no built-in default.

python scripts/draw_openloop_plot.py \
  --uri ws://127.0.0.1:32195 \
  --dataset-root /path/to/lerobot_dataset \
  --train-config /path/to/train_config.yml \
  --episode-indices 0,1,2 \
  --save-dir ./openloop_plots

Dataset and checkpoint utilities

• compute_norm_stats.py: compute action normalization statistics for a local LeRobot v3 dataset. The script reads state/action parquet columns directly when available, so image and video columns are not decoded.
• merge_sharded_weights.py: merge FSDP sharded checkpoint files into a single checkpoint directory.
• merge_tokenizer.py: merge FAST action tokens into a Qwen2.5-VL processor tokenizer.

python scripts/merge_tokenizer.py \
  --processor-path /path/to/Qwen2.5-VL-3B-Instruct \
  --action-tokenizer-path /path/to/fast_tokenizer \
  --output-dir /path/to/merged_processor

Most scripts support --help for their command-line options.