Add BridgeWAM model card

README.md

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 ---
-license: mit
+pretty_name: BridgeWAM Checkpoints
+library_name: pytorch
+tags:
+  - robotics
+  - robot-learning
+  - imitation-learning
+  - embodied-ai
+  - libero
+  - robotwin
+  - world-action-model
+  - latent-bridge-queries
 ---
+
+# BridgeWAM Checkpoints
+
+Released checkpoints for **BridgeWAM**, a video-action model that connects a
+pretrained video expert to a lightweight action expert through **Latent Bridge
+Queries (LBQs)**.
+
+This repository provides separate checkpoints for **LIBERO** and **RoboTwin**,
+together with the normalization statistics required by each checkpoint.
+
+## Files
+
+```text
+README.md
+libero/
+  model.pt
+  dataset_stats.json
+robotwin/
+  model.pt
+  dataset_stats.json
+```
+
+## Included checkpoints
+
+| Checkpoint | Benchmark | Camera inputs | Action dimensions |
+| --- | --- | --- | ---: |
+| `libero/model.pt` | LIBERO | Front and wrist cameras | 7 |
+| `robotwin/model.pt` | RoboTwin | Head and two wrist cameras | 14 |
+
+Use these checkpoints with the following task configurations in the BridgeWAM
+codebase:
+
+- **LIBERO:** `task=libero_uncond_2cam224_lbqs_only_2layer_alternating_cross_self_fullfinetune_1e-4`
+- **RoboTwin:** `task=robotwin_bridgewam_3cam384_lbq32_2layer_1e-4`
+
+Always use the `dataset_stats.json` from the **same benchmark folder** as the
+checkpoint. The two checkpoints have different action and proprioception
+dimensions and are not interchangeable.
+
+## BridgeWAM configuration
+
+Both checkpoints use a 30-layer Wan2.2 video expert and a two-layer action expert
+with alternating cross-attention and self-attention. They use **32 LBQs**, injected
+from video layer `0` and read out at the final video layer (`readout_layer=-1`).
+Action conditioning uses `lbq_only`, without direct Video-to-Action K/V routing.
+
+The standard action-inference path computes LBQ conditioning once from the
+observations, language instruction and proprioception, then reuses it throughout
+action denoising. It does **not** generate future video at test time.
+
+The release inference settings use a 32-step action horizon and 10 denoising
+steps. The LIBERO evaluator replans after 10 actions; the RoboTwin evaluator
+replans after 24 actions.
+
+## Download
+
+```bash
+pip install -U huggingface_hub
+hf download BridgeWAM/bridgewam --local-dir ./checkpoints/bridgewam
+```
+
+## Evaluation
+
+Install the [BridgeWAM code and dependencies](https://github.com/BridgeWam/BridgeWAM-code)
+and run the commands below from the code repository root, in its configured
+Linux/CUDA environment. The checkpoints require the matching Wan VAE, text
+encoder and tokenizer resources, as described in the code README. These external
+resources and simulator assets are not included in this model repository.
+
+Common environment settings:
+
+```bash
+export BRIDGEWAM_PYTHON="$(command -v python)"
+export PYTHONPATH="$PWD/src:$PWD${PYTHONPATH:+:$PYTHONPATH}"
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export MUJOCO_GL=egl
+export PYOPENGL_PLATFORM=egl
+export DIFFSYNTH_MODEL_BASE_PATH=/path/to/pretrained-models
+```
+
+### LIBERO
+
+Install LIBERO and configure its BDDL files, initial states and simulator assets.
+Replace `/path/to/LIBERO` with the environment repository path.
+
+```bash
+export LIBERO_ROOT=/path/to/LIBERO
+export PYTHONPATH="$LIBERO_ROOT:$PYTHONPATH"
+
+python experiments/libero/run_libero_manager.py \
+  task=libero_uncond_2cam224_lbqs_only_2layer_alternating_cross_self_fullfinetune_1e-4 \
+  ckpt=./checkpoints/bridgewam/libero/model.pt \
+  EVALUATION.dataset_stats_path=./checkpoints/bridgewam/libero/dataset_stats.json \
+  EVALUATION.output_dir="./runs/eval/libero/$(date +%Y%m%d_%H%M%S)" \
+  EVALUATION.num_trials=50 \
+  EVALUATION.num_inference_steps=10 \
+  model.skip_dit_load_from_pretrain=true \
+  seed=42 \
+  MULTIRUN.num_gpus=8 \
+  MULTIRUN.max_tasks_per_gpu=6
+```
+
+The default manager evaluates all 40 tasks across LIBERO-Spatial, LIBERO-Object,
+LIBERO-Goal and LIBERO-Long (`libero_10`). LIBERO-Plus and LIBERO-Pro have separate
+environment setup instructions and evaluation entrypoints in the code repository.
+
+### RoboTwin
+
+Install the complete RoboTwin environment and assets at `third_party/RoboTwin`,
+and expose the `bridgewam_policy` adapter under its `policy/` directory. Empty
+placeholder files are not a working simulator installation.
+
+```bash
+python experiments/robotwin/run_robotwin_manager.py \
+  task=robotwin_bridgewam_3cam384_lbq32_2layer_1e-4 \
+  ckpt=./checkpoints/bridgewam/robotwin/model.pt \
+  EVALUATION.dataset_stats_path=./checkpoints/bridgewam/robotwin/dataset_stats.json \
+  EVALUATION.output_dir="./runs/eval/robotwin/$(date +%Y%m%d_%H%M%S)" \
+  EVALUATION.eval_num_episodes=100 \
+  EVALUATION.num_inference_steps=10 \
+  model.skip_dit_load_from_pretrain=true \
+  seed=42 \
+  MULTIRUN.num_gpus=8 \
+  MULTIRUN.max_tasks_per_gpu=4
+```
+
+The manager schedules clean and randomized evaluation settings. The concurrency
+values above are launch settings, not minimum GPU requirements; reduce
+`MULTIRUN.max_tasks_per_gpu` if GPU memory is insufficient.
+
+## Notes and limitations
+
+- These are task-specific research checkpoints, not general-purpose robot policies.
+- Use the matching camera layout, action/state representation and normalization.
+- Load weights through the BridgeWAM model loader and matching task configuration;
+  these are not Transformers `AutoModel` checkpoints.
+- Successful checkpoint loading does not establish closed-loop task success.
+  Benchmark results require the corresponding simulator and a complete evaluation.
+- Real-robot deployment requires independent validation and appropriate safeguards.
+- Upstream software, model and dataset licenses continue to apply.
+
+## Project
+
+- Code: [BridgeWAM-code](https://github.com/BridgeWam/BridgeWAM-code)
+- Checkpoints: [BridgeWAM/bridgewam](https://huggingface.co/BridgeWAM/bridgewam)
+
+BridgeWAM builds on Wan, Fast-WAM and LeRobot components. See the code repository
+for upstream attribution, environment setup and additional evaluation instructions.