Document checkpoint download, licensing, and verified inference

CHECKSUMS.sha256

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+efd3f50def5f65d0aa57da1d2bbe1b159cf7b14e870d82495d4b4167c0aa62dd  ckpt_latest125.pt

DINOv2-LICENSE

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LICENSE

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+# Project Registration License (PRL) v1.0
+
+Copyright (c) 2026 ZJU3DV Group at Zhejiang University
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, subject to the following conditions.
+
+## Research, Educational, and Personal Use
+
+Use of the Software solely for academic research, education, evaluation, or personal purposes is permitted free of charge and does not require registration.
+
+Research results, papers, reports, and other publications produced using the Software may be published without registration.
+
+## Project Use and Registration
+
+Use of the Software by a company, organization, or other entity as part of any internal or external product, service, system, application, development project, or other organizational project ("Project Use") is permitted free of charge, provided that the Project Use is registered before such use begins.
+
+This requirement applies regardless of whether the project:
+
+- is commercial or non-commercial;
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+- generates revenue; or
+- is made available to third parties.
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+Registration shall be submitted through:
+
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+
+## Redistribution
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+Recipients of redistributed copies must independently comply with the registration requirements of this License.
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README.md

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+---
+license: other
+license_name: prl-1.0
+license_link: LICENSE
+language:
+- en
+tags:
+- robotics
+- navigation
+- supernav
+- arxiv:2610.12126
+---
+
+# SuperNav Learned Executor
+
+Learned local-navigation checkpoint for [SuperNav: An Agentic Navigation System for Any Task in Any Scene](https://arxiv.org/abs/2610.12126).
+
+- **Code:** [zju3dv/SuperNav](https://github.com/zju3dv/SuperNav), release commit [`80a59a4`](https://github.com/zju3dv/SuperNav/tree/80a59a4c1dfe4f03528a0800a0d30d5a37c6244f).
+- **Checkpoint:** `ckpt_latest125.pt` (249,157,807 bytes).
+- **Role:** the learned local executor, served through SuperNav's `nomad` backend. The high-level agent, simulator, scene assets, and task manifests are separate requirements.
+- **Format:** PyTorch checkpoint containing `state_dict` and `train_config`. Use the SuperNav loader, which reads the checkpoint's architecture settings; this is not a Transformers `from_pretrained` model.
+
+## Architecture
+
+The checkpoint metadata selects a fine-tuned DINOv2 ViT-S/14 encoder (`dino-s-finetune`) with 224-pixel input images, GCA context (`context_size=4`, `memory_budget=12`), and a flow-matching action head with four inference steps. It predicts an eight-waypoint action horizon with waypoint scale 0.25 meters. The SuperNav loader reads these settings from the checkpoint.
+
+## Download
+
+Install the Hugging Face CLI with `python -m pip install -U huggingface_hub`, then run:
+
+```bash
+hf download the0xka1/SuperNav-Learned-Executor \
+  ckpt_latest125.pt CHECKSUMS.sha256 --local-dir ./checkpoints
+(cd checkpoints && sha256sum -c CHECKSUMS.sha256)
+```
+
+The independently downloaded artifact is pinned at Hugging Face revision [`fe29dd8`](https://huggingface.co/the0xka1/SuperNav-Learned-Executor/tree/fe29dd8e9266e3b70da51b2fa1b58e01b879d7fa). The checkpoint's SHA256 is:
+
+```text
+efd3f50def5f65d0aa57da1d2bbe1b159cf7b14e870d82495d4b4167c0aa62dd
+```
+
+## Start the policy service
+
+Use Python 3.12. The verified CUDA 12.1 environment used PyTorch `2.5.1+cu121` and torchvision `0.20.1+cu121`. Install this pair with the [official PyTorch wheel command](https://pytorch.org/get-started/previous-versions/#v251), or choose compatible builds for your device:
+
+```bash
+python -m pip install torch==2.5.1 torchvision==0.20.1 \
+  --index-url https://download.pytorch.org/whl/cu121
+```
+
+Install the published SuperNav code and its runtime dependencies in the policy-service environment:
+
+```bash
+git clone https://github.com/zju3dv/SuperNav.git
+cd SuperNav
+git checkout 80a59a4c1dfe4f03528a0800a0d30d5a37c6244f
+python -m pip install -e '.[agents,evaluation]'
+
+export TORCH_HOME=/absolute/path/to/torch-cache
+export NAV_LOCALNAV_CKPT=/absolute/path/to/checkpoints/ckpt_latest125.pt
+python -m supernav.methods.localnav.server \
+  --backend nomad --checkpoint "$NAV_LOCALNAV_CKPT" \
+  --host 127.0.0.1 --port 18914 --device cuda
+```
+
+Set `TORCH_HOME` to a writable cache directory. At the pinned SuperNav revision, the DINOv2 loader uses Torch Hub to obtain the [upstream repository](https://github.com/facebookresearch/dinov2) and [initial ViT-S/14 backbone weights](https://dl.fbaipublicfiles.com/dinov2/dinov2_vits14/dinov2_vits14_pretrain.pth) (about 84.2 MiB) before loading this checkpoint. The first startup requires access to GitHub and `dl.fbaipublicfiles.com`, or an already populated Torch Hub cache. xFormers is optional; a warning that it is unavailable does not prevent the tested inference path from running.
+
+For CPU inference, use `--device cpu`. In another terminal, check:
+
+```bash
+curl --fail http://127.0.0.1:18914/healthz
+```
+
+The response should report `backend: nomad` and the downloaded checkpoint path. At the pinned code revision, its `model` field is a legacy descriptive label; the architecture above comes from the actual checkpoint metadata. Check the startup log for successful checkpoint loading; a healthy HTTP endpoint alone does not validate navigation. Keep the service running for the experiment. If using an HTTP proxy, include `127.0.0.1,localhost` in `NO_PROXY` and `no_proxy` so local policy and bridge requests bypass it.
+
+## Run a navigation task
+
+Follow the published [Habitat setup guide](https://github.com/zju3dv/SuperNav/blob/80a59a4c1dfe4f03528a0800a0d30d5a37c6244f/docs/release/en/habitat.md) to prepare Habitat-GS, scene assets/NavMeshes, the agent, and an external task manifest. In the agent environment:
+
+```bash
+export SUPERNAV_HABITAT_PYTHON=/absolute/path/to/habitat-env/bin/python
+export SUPERNAV_SCENE_DATASET_CONFIG=/absolute/path/to/scene_dataset_config.json
+export NAV_LOCALNAV_URL=http://127.0.0.1:18914
+
+supernav run --experiment habitat-learned-executor \
+  --instructions /absolute/path/to/tasks.json \
+  --task-ids example-task --arms default \
+  --model gpt-6-astra --output-dir data/runs/learned-executor
+```
+
+Replace the paths and task ID with your own assets and manifest. If the Habitat bridge runs on a different host, set `NAV_LOCALNAV_URL` to an address it can reach. `--dry-run` only prepares inputs; it does not verify policy loading or task execution.
+
+## Validation status
+
+On 2026-10-09, an independent anonymous download at the artifact revision above matched the published size and SHA256 and was readable with `torch.load(weights_only=True)`. The unmodified published SuperNav policy service loaded it on CUDA with zero missing and zero unexpected state-dict keys, using a fresh Torch Hub cache. Its real `/act` inference returned sampled trajectories, and a SuperNav agent completed one HM3D navigation episode with eight learned local-navigation calls and a successful independent evaluation. This checks download, loading, inference, and task execution for one episode; it is not a benchmark evaluation. Scene assets and private task/evaluation data are not distributed here.
+
+## License
+
+This checkpoint is distributed under the [Project Registration License (PRL) v1.0](LICENSE). The DINOv2 backbone retains its Apache-2.0 terms. External dependencies, simulator assets, and datasets retain their respective licenses; see [third-party notices](THIRD_PARTY_NOTICES.md).

THIRD_PARTY_NOTICES.md

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+# Third-party notices
+
+This repository distributes a SuperNav checkpoint and its documentation. The Project Registration License does not replace third-party component licenses.
+
+## DINOv2 backbone — Apache License 2.0
+
+The checkpoint uses a fine-tuned DINOv2 ViT-S/14 backbone from [facebookresearch/dinov2](https://github.com/facebookresearch/dinov2). Its backbone parameters have been modified by SuperNav training. The upstream project states that DINOv2 code and model weights are released under Apache License 2.0: [license statement](https://github.com/facebookresearch/dinov2#license), [upstream license](https://github.com/facebookresearch/dinov2/blob/main/LICENSE). A copy of that license accompanies this repository in [DINOv2-LICENSE](DINOv2-LICENSE). The SuperNav project license does not relicense the upstream DINOv2 component.
+
+## Runtime code and assets
+
+Use the published SuperNav implementation and retain its [third-party notices](https://github.com/zju3dv/SuperNav/blob/80a59a4c1dfe4f03528a0800a0d30d5a37c6244f/THIRD_PARTY_NOTICES.md). PyTorch, torchvision, Habitat-GS, and other separately installed dependencies remain subject to their original licenses. Simulator assets and task datasets are not included in this model repository.