| --- |
| license: apache-2.0 |
| library_name: pytorch |
| base_model: Wan-AI/Wan2.2-TI2V-5B |
| tags: |
| - robotics |
| - world-action-model |
| - action-chunking |
| - piper |
| - fastwam |
| --- |
| |
| # SpaceDreamer Piper WAM |
|
|
| This repository contains the runtime artifacts for the Piper RGB-state WAM |
| policy. Given one `cam_high` RGB frame, the current 14D dual-arm qpos, and one |
| of four supported task IDs, the policy generates a 32-step action chunk. |
|
|
| Inference code and documentation: |
| [qshou-coder/SpaceDreamer, `piper_inference` branch](https://github.com/qshou-coder/SpaceDreamer/tree/piper_inference) |
|
|
| ## Artifacts |
|
|
| | File | Purpose | |
| |---|---| |
| | `policy.pt` | Complete RGB DiT, ActionDiT, state encoder, and action normalizer | |
| | `Wan2.2_VAE.pth` | Wan2.2 VAE used to encode the live RGB frame | |
| | `prompt_contexts.pt` | Frozen UMT5 contexts for the four supported tasks | |
| | `inference_config.yaml` | Portable model and input/output contract | |
| | `SHA256SUMS` | Artifact integrity checksums | |
|
|
| The policy checkpoint already contains the action normalization buffers. It |
| does not require the Wan base DiT shards or the UMT5 encoder at runtime. |
|
|
| ## Input/output contract |
|
|
| - Image: `uint8[H,W,3]`, from `cam_high`, explicitly marked RGB or BGR. |
| - State: raw, unnormalized qpos with shape `[14]`. |
| - State/action order: left joints 1-6, left gripper, right joints 1-6, |
| right gripper. |
| - Output: `float32[32,14]` absolute joint-position commands at 25 Hz. |
| - Sampling: joint RGB/action flow matching, 20 inference steps, shift 5. |
|
|
| Supported task IDs: |
|
|
| - `assemble_battery_long` |
| - `battery_assemble` |
| - `pack_3_objects_plus` |
| - `stack_3_cups_gen` |
|
|
| See the GitHub usage guide for the Python API and offline CLI. |
|
|
| ## Safety |
|
|
| The model only predicts actions. It does not enforce robot joint limits, |
| velocity limits, observation freshness, collision avoidance, or emergency |
| stop state. A robot-specific safety/control layer must validate every chunk |
| before execution. |
|
|
| ## Upstream attribution |
|
|
| The included VAE is redistributed from |
| [`Wan-AI/Wan2.2-TI2V-5B`](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B), |
| which is licensed under Apache-2.0. The inference implementation also uses |
| FastWAM components distributed with the accompanying GitHub code under MIT. |
|
|
|
|
