StreamWAM

Streaming World-Action Models for Robotic Manipulation

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

StreamWAM 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 StreamWAM GitHub repository.

Released checkpoints

Directory	Model	Description
joint-cd/	FastWAM-Joint-CD	Fast one-step joint world-and-action prediction for LIBERO.
ac-stream/	StreamWAM	Recommended StreamWAM checkpoint for efficient LIBERO evaluation.

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.

Method	LIBERO-10	Spatial	Goal	Object	Average (%) ↑	Chunk Time (ms) ↓	Episode Time (s) ↓ Long / Short
FastWAM	96.20	96.20	94.20	96.20	95.70	493.0	16.31 / 8.25
FastWAM-Joint-CD	97.20	99.60	98.60	100.00	98.85	114.2	6.89 / 3.74
StreamWAM	96.60	98.80	97.40	100.00	98.20	41.0	5.36 / 3.15

Installation

Clone the code repository and install the environment:

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

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/StreamWAM \
  --local-dir checkpoints/streamwam

The resulting layout is:

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

Run StreamWAM

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="$PWD/checkpoints/streamwam/ac-stream/model.pt" \
STATS_PATH="$PWD/checkpoints/streamwam/ac-stream/dataset_stats.json" \
  bash examples/libero/scripts/launch_streamwam_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/streamwam_libero_joint_cd_wan22_5b.yaml \
  --checkpoint-format fastwam \
  --checkpoint checkpoints/streamwam/joint-cd/model.pt \
  --backbone-path checkpoints/Wan2.2-TI2V-5B \
  --stats-path checkpoints/streamwam/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

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