Fysics-AI/UniWAM

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 and GR00T N1.7 navigation baseline checkpoints.
• 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 67 GB):

python -m pip install -U huggingface_hub
hf download Fysics-AI/UniWAM --local-dir ./UniWAM-weights

This includes both UniWAM checkpoints, both baselines, 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.