OpenNWM

The Nav15 Pixel+Action release of OpenNWM: a latent action model (LAM), a Stage 1 navigation world model pretrained with latent actions, and a Stage 2 world model adapted to real navigation actions.

• Code and setup: B0B8K1ng/OpenNWM
• ModelScope mirror: LittleBoss/OpenNWM
• Training data: NavAnywhere

Checkpoints

File	Use	Bytes
opennwm-nav15-stage2-joint100k.pth.tar	Stage 2 real-action navigation; 3,000 adapter warmup + 100,000 joint steps	3,137,820,686
opennwm-nav15-stage1-pretrain60k.pth.tar	Stage 1 latent-action pretraining; 60,000 steps	3,137,820,971
lam-nav15-pixel-action-step60k.ckpt	Nav15 PixelActionLAM; 60,000 steps; 32-dimensional latent actions	8,518,856,077

Use Stage 2 for real-action prediction and planning. Stage 1 uses latent actions and has not been adapted for direct real-action navigation. The LAM maps image pairs to 32-dimensional latent actions. These are the final Nav15 checkpoints, distinct from the older Nav1 weights in LittleBoss/LAM-weights.

Files preserve their original checkpoint format and bytes, including training state and historical configuration metadata. No optimizer tensors were removed and no weight conversion was performed. Use the separate portable YAML configs below instead of relying on historical machine paths embedded in checkpoints. All payload files are covered by SHA256SUMS; manifest.json records checkpoint roles, byte sizes and hashes.

Model and inference settings

The world models use CDiT-B/2 with four 224×224 context frames and stabilityai/sd-vae-ft-ema (latent scaling 0.18215). The VAE is an external dependency and is downloaded separately. Stage 2 uses the checkpoint's ema state, three real action dimensions, and 250 DDPM sampling steps. The real-action normalization bounds are [-2.5, -4, -π] to [5, 4, π]. LAM images use a 4:3 center crop followed by resize to 240×320; use the repository preprocessing code.

Download and use

from huggingface_hub import snapshot_download
snapshot_download("LittleB0sS/OpenNWM", local_dir="./weights/OpenNWM")

Install OpenNWM and configure its data and runtime paths following the code repository README. From the OpenNWM source repository, import Stage 2 into a new inference directory with its portable config:

python run.py nwm import-checkpoint \
  --checkpoint /path/to/weights/OpenNWM/opennwm-nav15-stage2-joint100k.pth.tar \
  --config /path/to/weights/OpenNWM/configs/opennwm-nav15-stage2.yaml \
  --run-dir /path/to/runs/opennwm-nav15-stage2
python run.py nwm infer exp_dir=/path/to/runs/opennwm-nav15-stage2 ckp=latest eval_type=time

NWM_VAE_PATH can point to a local VAE or use the default public identifier. Dataset evaluation also requires NWM_DATA_ROOT and NWM_SPLIT_ROOT; use the original split lists for historical score reproduction. The portable configs are for inference and do not claim exact training resumption. Refer to the repository NWM README for prediction, evaluation and planning, and the LAM README for using lam-nav15-pixel-action-step60k.ckpt with LAM evaluation or latent extraction. Stage 1's config additionally uses NWM_NAVANYWHERE_ROOT, NWM_NAVANYWHERE_RECIPE and NWM_LATENT_PROXY_ROOT.

Provenance and licenses

NWM code derives from Meta NWM and CompACT; its upstream CC BY-NC 4.0 license is preserved in licenses/NWM-CC-BY-NC-4.0.txt. DreamDojo LAM code's Apache 2.0 license is preserved in licenses/DreamDojo-Apache-2.0.txt. The repository code provenance pins DreamDojo 8f3eeb7, CompACT 8367a5e, and Meta NWM 3f6cd8e. This release retains both upstream notices and does not relicense upstream material. External VAE and data sources retain their respective terms.

The weights support research on visual navigation and future observation prediction. They are not a complete robot controller; predictions can drift outside the training distribution. No new benchmark scores or deployment claims are introduced by this release.

中文说明

本仓库发布 Nav15 Pixel+Action 的最终 LAM、Stage 1 和 Stage 2 权重。 真实动作导航预测与规划使用 Stage 2 的 EMA 权重。所有 checkpoint 保留原始 字节和训练状态；请传入本仓库提供的可迁移 YAML 配置，避免使用 checkpoint 中历史机器路径。完整使用方法见代码仓库，文件完整性由 SHA256SUMS 校验。