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.

Each directory contains:

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

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.

Clone the code repository and install the environment:

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:

Download both released checkpoints:

The resulting layout is:

Evaluate all 40 LIBERO tasks once on four GPUs:

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.

For more evaluation options, see the LIBERO guide.

Released under the Apache License 2.0.

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