language:
- en
license: other
license_name: uniwam-and-upstream-model-licenses
license_link: https://github.com/Fysics-AI/UniWAM/blob/main/THIRD_PARTY_NOTICES.md
pipeline_tag: robotics
inference: false
base_model: Wan-AI/Wan2.2-TI2V-5B
tags:
- robotics
- navigation
- world-action-model
- uniwam
- map-bench
Paper · Project · GitHub · MAP-Bench
UniWAM unifies navigation and manipulation through a shared world-action backbone and Manipulation Anchor Pose (MAP) supervision. This repository provides the checkpoints and shared components for navigation on MAP-Bench:
- UniWAM-Base and UniWAM-Scaled, with inference configurations and normalization files.
- π₀.₅ navigation baseline checkpoint.
- Shared Wan2.2 VAE, UMT5-XXL text encoder, and tokenizer.
The released UniWAM checkpoints use a single navigation expert and one forward RGB view, since MAP-Bench evaluates navigation only. The full mobile manipulation architecture and training/inference code are available on GitHub.
Download
Run from your workspace directory to download the complete model package (approximately 50 GB):
python -m pip install -U huggingface_hub
hf download Fysics-AI/UniWAM --local-dir ./UniWAM-weights
This includes both UniWAM checkpoints, the π₀.₅ comparison checkpoint, and the shared components. For selective downloads, use the GitHub download helper.
Usage
See GitHub for environment setup, inference and MAP-Bench evaluation, and training.
UniWAM weights use Apache-2.0. Bundled third-party models retain their upstream licenses.