Streaming-WAM

Streaming World-Action Models for Robotic Manipulation

Streaming-WAM is a research framework for streaming World-Action Models. It provides a unified testbed for studying and comparing efficient robot-control strategies.

Streaming-WAM uses the actions currently being executed by the robot to guide its next prediction. This allows action execution and model inference to proceed together, reducing the time required to complete a robot task while maintaining strong control performance.

This repository provides the released LIBERO checkpoints. The corresponding inference and evaluation code is available in the Streaming-WAM GitHub repository.

Released checkpoints

Each directory contains:

model.pt
dataset_stats.json

The checkpoints use the Wan2.2 TI2V 5B backbone. Wan2.2 model assets are not included here and must be downloaded separately.

LIBERO results

We evaluate all methods on LIBERO-10, LIBERO-Spatial, LIBERO-Goal, and LIBERO-Object with 50 trials per task. Chunk Time is the average model inference time for one action chunk. Episode Time is the average end-to-end task duration for long- and short-horizon tasks.

Installation

Clone the code repository and install the environment:

git clone https://github.com/SJTU-DENG-Lab/Streaming-WAM.git
cd Streaming-WAM

python -m pip install -U uv
uv sync

The reference environment uses Python 3.10, PyTorch 2.7.1 with CUDA 12.8, and Triton 3.3.1.

Prepare LIBERO and the Wan2.2 backbone:

git clone https://github.com/Lifelong-Robot-Learning/LIBERO.git third_party/LIBERO
uv pip install -e third_party/LIBERO --no-deps

uv run huggingface-cli download Wan-AI/Wan2.2-TI2V-5B \
  --local-dir checkpoints/Wan2.2-TI2V-5B

Download checkpoints

Download both released checkpoints:

hf download SJTU-DENG-Lab/Streaming-WAM \
  --local-dir checkpoints/streamingwam

The resulting layout is:

checkpoints/streamingwam/
├── joint-cd/
│   ├── model.pt
│   └── dataset_stats.json
└── ac-stream/
    ├── model.pt
    └── dataset_stats.json

Run Streaming-WAM

Evaluate all 40 LIBERO tasks once on four GPUs:

PYTHON_BIN=.venv/bin/python \
GPU_IDS=0,1,2,3 \
BACKBONE_PATH="$PWD/checkpoints/Wan2.2-TI2V-5B" \
LIBERO_HOME_PATH="$PWD/third_party/LIBERO" \
CHECKPOINT_PATH="$PWD/checkpoints/streamingwam/ac-stream/model.pt" \
STATS_PATH="$PWD/checkpoints/streamingwam/ac-stream/dataset_stats.json" \
  bash examples/libero/scripts/launch_streamingwam_libero_ac_stream_4gpu.sh \
  --ac-stream-accelerated

The launcher evaluates one trial for every task in libero_spatial, libero_object, libero_goal, and libero_10. GPU IDs can be changed through GPU_IDS.

Run FastWAM-Joint-CD

python examples/libero/multigpu_rollout.py \
  --gpus 0,1,2,3 \
  --suites libero_spatial,libero_object,libero_goal,libero_10 \
  --num-trials 1 \
  --config examples/libero/configs/recipes/streamingwam_libero_joint_cd_wan22_5b.yaml \
  --checkpoint-format fastwam \
  --checkpoint checkpoints/streamingwam/joint-cd/model.pt \
  --backbone-path checkpoints/Wan2.2-TI2V-5B \
  --stats-path checkpoints/streamingwam/joint-cd/dataset_stats.json \
  --libero-home third_party/LIBERO \
  --num-steps-wait 30 \
  --replan-steps 16 \
  --num-inference-steps 1 \
  --sampling-method consistency \
  --fixed-seed \
  --mujoco-gl egl \
  --save-video

For more evaluation options, see the LIBERO guide.

License

Released under the Apache License 2.0.

Acknowledgements

Streaming-WAM builds on ideas and open-source work from FastWAM, StarWAM, LIBERO, and Wan2.2.