add dataset card + asset previews (v1 rgb tier)

README.md

@@ -0,0 +1,414 @@
+---
+license: cc-by-4.0
+language:
+- en
+size_categories:
+- 100K<n<1M
+task_categories:
+- video-classification
+- video-text-to-text
+- text-to-video
+pretty_name: PhysicalAI SDG-Warehouse
+tags:
+- physical-ai
+- synthetic-data
+- video
+- warehouse
+- industrial-safety
+- isaac-sim
+- forklift
+- fire
+- multi-view
+- webdataset
+- cosmos
+- nvidia
+---
+
+# 🏭 PhysicalAI SDG-Warehouse
+
+**A 412-hour, ~123K-clip synthetic warehouse safety video dataset — multi-view CCTV + eye-level rigs, four staged industrial-safety scenarios, full reproducibility from a single seed, packaged as streamable WebDataset shards.**
+
+<p align="center">
+  <img src="./assets/scenario_nearmiss.jpg"     width="24%" alt="Forklift–human near-miss"/>
+  <img src="./assets/scenario_fire.jpg"         width="24%" alt="Warehouse fire"/>
+  <img src="./assets/scenario_collision.jpg"    width="24%" alt="Forklift–shelf collision"/>
+  <img src="./assets/scenario_box_pickup.jpg"   width="24%" alt="Warehouse box pickup"/>
+</p>
+
+<p align="center">
+  <em>Forklift–human near-miss &nbsp;·&nbsp; Warehouse fire &nbsp;·&nbsp; Forklift–shelf collision &nbsp;·&nbsp; Box pickup</em>
+</p>
+
+<p align="center">
+  <a href="https://huggingface.co/datasets/nvidia/PhysicalAI-SDG-WareHouse"><img src="https://img.shields.io/badge/🤗%20HuggingFace-Dataset-yellow"></a>
+  <img src="https://img.shields.io/badge/license-CC--BY--4.0-blue">
+  <img src="https://img.shields.io/badge/clips-~123K-success">
+  <img src="https://img.shields.io/badge/duration-~412h-success">
+  <img src="https://img.shields.io/badge/resolution-1920×1080-success">
+  <img src="https://img.shields.io/badge/fps-30-success">
+  <img src="https://img.shields.io/badge/format-WebDataset-orange">
+  <img src="https://img.shields.io/badge/built%20with-NVIDIA%20Isaac%20Sim-76B900">
+</p>
+
+---
+
+## At a glance
+
+| | | | |
+|:--:|:--:|:--:|:--:|
+| **≈123K**<br>video clips | **≈412 h**<br>of footage | **≈29.2K**<br>runs (samples) | **4**<br>scenarios |
+| **1920×1080**<br>30 fps H.264 | **WebDataset**<br>tar shards (~5 GiB) | **5–10**<br>synced cameras / run | **100% reproducible**<br>(seed → pixels) |
+
+> **v1 release scope (this card):** RGB video only, organized as run-grouped WebDataset shards. The full **artifacts** tier — metric depth, instance + shaded segmentation, Canny edges, 2D/3D bounding boxes, and per-frame camera intrinsics+extrinsics — is rendered, ground-truth-aligned to every RGB frame, and will land in an `artifacts/` tier of the same repo as a follow-up. The annotation visualizations below are from those native renders.
+
+---
+
+## ⚡ Quickstart
+
+Download a single scenario (a few hundred GB) — recommended for most users:
+
+```bash
+pip install -U "huggingface_hub[hf_xet]"
+
+huggingface-cli download nvidia/PhysicalAI-SDG-WareHouse \
+  --repo-type dataset \
+  --include "rgb/forklift_human_nearmiss/**" "metadata/**" \
+  --local-dir ./PhysicalAI-SDG-WareHouse
+```
+
+Available scenario directories:
+
+| Scenario | Repo path | Shards | ~Size |
+|---|---|---:|---:|
+| Forklift–human near-miss | `rgb/forklift_human_nearmiss/` | 113 | ~549 GiB |
+| Warehouse fire | `rgb/warehouse_fire/` | 125 | ~619 GiB |
+| Forklift–shelf collision | `rgb/forklift_shelf_collision/` | 114 | ~559 GiB |
+| Warehouse box pickup | `rgb/warehouse_box_pickup/` | 107 | ~520 GiB |
+
+Or pull the whole dataset (~2.24 TiB):
+
+```bash
+huggingface-cli download nvidia/PhysicalAI-SDG-WareHouse \
+  --repo-type dataset \
+  --local-dir ./PhysicalAI-SDG-WareHouse
+```
+
+For **streaming** (no full download) see [Loading examples](#loading-examples).
+
+---
+
+## Why this dataset?
+
+> Real industrial-safety footage is **rare, sensitive, and rarely annotated**. SDG-Warehouse fixes that by guaranteeing the event happens, controlling every parameter, and shipping per-pixel ground truth for every frame.
+
+- **🎯 Deterministic ground truth.** *(Coming with the artifacts tier.)* Depth, instance segmentation, shaded segmentation, Canny edges, and 2D / 3D bounding boxes — *natively rendered*, not predicted, perfectly aligned to the RGB.
+- **📹 Multi-view by construction.** Every run is filmed from 5–10 synchronized cameras (ceiling CCTV + worker-height eye-level / circular impact rigs), with stable per-camera aliases that match across releases.
+- **🔁 Reproducible.** Each run is driven by a single random seed that controls scene composition, lighting, agent identity, motion, camera pose, and event timing. Seed is exposed in every sample's `meta.json`.
+- **🚨 Rare-event coverage.** Forklift–human near-misses, fire evacuations, and shelf collisions — events that are operationally dangerous and legally fraught to capture in the real world.
+- **📦 Streamable.** Shipped as standard WebDataset `.tar` shards (~5 GiB each), so you can train without downloading hundreds of GB up front. A Parquet `metadata/clips.parquet` index lets you filter by scenario / camera / seed without opening a single tar.
+- **🏗️ Built on NVIDIA Isaac Sim** with Isaac Sim Replicator Object (IRO) and Replicator Agent (IRA), so the same pipeline can be extended to your own scenarios.
+
+This dataset is described in §A.1.5 of the [Cosmos3 technical report](#citation). <!-- TODO: replace #citation with arXiv URL when public -->
+
+---
+
+## What you can build with it
+
+| Use case | Why SDG-Warehouse helps |
+|---|---|
+| **Industrial-safety event detection** (near-miss, collision, fire, evacuation) | Rare in real footage, fully covered here, with seed-controlled difficulty (e.g. last-moment dodge distance). |
+| **Multi-camera 3D tracking** | 5–10 synchronized camera viewpoints per run; full intrinsics + extrinsics ship with the artifacts tier. |
+| **Video / world foundation models (Cosmos and friends)** | 412 hours of physically-grounded, long-horizon, multi-camera video. |
+| **Forklift behavior / collision prediction** | Parameterized forklift trajectories, dodge events, and rigid-body shelf-impact dynamics. |
+| **Monocular depth, segmentation & edge models** | Pixel-perfect colorized depth, instance / shaded segmentation, and Canny edges — *available in the upcoming artifacts tier*, large-scale, perfectly aligned, automatic. |
+| **Sim-to-real & domain-randomization research** | Full per-light randomization (color temperature, intensity, exposure, color), per-asset variation, and parametric camera placement give a controlled testbed. |
+| **Synthetic-data ablations & scaling laws** | Independent reproducible runs make it straightforward to subsample, re-render, or augment for data-scaling studies. |
+| **VLM / video-LLM training & eval** | Dense scenario / camera / seed metadata in `metadata/clips.parquet`; per-frame captions can be derived from the run-level metadata. |
+
+---
+
+## Modalities
+
+### Available now (this release — `rgb/` tier)
+
+| Stream | Format | What it is |
+|---|---|---|
+| **RGB** | MP4 (H.264) | Photoreal Isaac Sim render of the scene, one mp4 per camera per run |
+| **Run metadata** | JSON (`meta.json`) | scenario, seed, source_kind, list of cameras with aliases + source paths, S3 provenance |
+| **IRA writer metadata** | TXT (`metadata.txt`) | Replicator-writer name / version / global seed (present for most multi-cam runs) |
+| **Per-clip Parquet index** | `metadata/clips.parquet` | one row per `(run × camera)` for fast filtering |
+| **Per-run Parquet index** | `metadata/runs.parquet` | one row per run with `clip_keys[]`, `n_cameras`, `total_bytes`, `shard_path_in_repo` |
+
+### Coming with the artifacts tier (`artifacts/<scenario>/*.tar`)
+
+| Stream | Format (per camera per run) | What it is |
+|---|---|---|
+| **Metric depth** | `depth.mp4` (log-normalized colorized) | Per-pixel distance to camera |
+| **Instance segmentation** | `segmentation.mp4` (colorized) + `instance_seg.tar` (per-frame 16-bit PNG IDs) | Stable instance IDs across frames and views |
+| **Shaded segmentation** | `shaded_seg.mp4` | 3D-aware segmentation with normal-based shading |
+| **Canny edges** | `edges.mp4` | Canny edges computed on the shaded segmentation |
+| **2D / 3D bounding boxes** | `object_detection.json` (consolidated per camera) | Tight + loose 2D AABBs + oriented 3D OBBs, per agent / prop, per frame |
+| **Camera parameters** | `camera_params.json` (consolidated per camera) | Intrinsics + view transform, per frame |
+
+> **Note:** the README and `metadata/runs.parquet` schemas are forward-compatible — when the artifacts tier lands, the `run_id` and `clip_key` keys join cleanly to the RGB tier.
+
+---
+
+## Scenarios
+
+Each scenario is a self-contained event in a shared warehouse environment with shelves and props; scenarios differ in which agents are spawned, what event is staged, and how cameras are placed.
+
+### 🚜 Forklift–human near-miss
+A worker stands at a fixed location while a forklift navigates toward them along a planned path. A configurable **last-moment dodge distance** distinguishes a near-miss from an outright contact event, letting the same scene composition produce both event classes. Captured by a mix of ceiling-mounted CCTV-style cameras (`ceiling_00`–`ceiling_04`) and worker-height eye-level cameras (`eye_00`–`eye_04`).
+
+### 🔥 Warehouse fire
+A localized volumetric fire ignites at a randomized position and time while a small crew of workers performs random walks. On ignition, each worker reacts — orienting toward the flame and then running toward a designated exit waypoint along a navigation-mesh path — producing rare emergency-response footage that combines dynamic flames, smoke, and coordinated human evacuation in a single shot. Cameras at ceiling height for floor coverage (`ceiling_00`–`ceiling_04`).
+
+### 💥 Forklift–shelf collision
+A forklift drives at a parameterized initial distance toward a populated storage shelf and impacts it, producing visible **rigid-body knock-over and debris dynamics**. An optional character can be placed along the forklift's path to extend the scenario to a forklift–shelf–human three-body event. Cameras placed circularly around the impact site at varying heights, distances, and look-down angles (`cam_00`–`cam_05`).
+
+### 📦 Warehouse box pickup
+A worker navigates to a randomly placed box, performs a contact-rich pickup motion, and carries it through the warehouse. Provides routine, **non-incident action coverage** as a counterpoint to the three safety scenarios. Cameras (`cam_00`–`cam_09`) form a mixed CCTV + eye-level rig.
+
+---
+
+## Multi-view coverage
+
+Every run is captured by multiple synchronized cameras. Below: one forklift–human near-miss run from **5 ceiling-mounted CCTV** viewpoints (top) and **5 worker-height eye-level** viewpoints (bottom).
+
+<p align="center">
+  <img src="./assets/multiview_cctv_view1.jpg" width="18%"/>
+  <img src="./assets/multiview_cctv_view2.jpg" width="18%"/>
+  <img src="./assets/multiview_cctv_view3.jpg" width="18%"/>
+  <img src="./assets/multiview_cctv_view4.jpg" width="18%"/>
+  <img src="./assets/multiview_cctv_view5.jpg" width="18%"/>
+</p>
+<p align="center">
+  <img src="./assets/multiview_eyelevel_view1.jpg" width="18%"/>
+  <img src="./assets/multiview_eyelevel_view2.jpg" width="18%"/>
+  <img src="./assets/multiview_eyelevel_view3.jpg" width="18%"/>
+  <img src="./assets/multiview_eyelevel_view4.jpg" width="18%"/>
+  <img src="./assets/multiview_eyelevel_view5.jpg" width="18%"/>
+</p>
+
+---
+
+## Ground-truth modalities (per scenario)
+
+These previews show the **native renders** that ship in the upcoming artifacts tier; the RGB column is included with the current release.
+
+|              | RGB                                       | Depth                                  | Instance seg.                                    | Shaded seg.                                     | Canny edges                                |
+|--------------|-------------------------------------------|----------------------------------------|--------------------------------------------------|-------------------------------------------------|--------------------------------------------|
+| Near-miss    | <img src="./assets/scenario_nearmiss.jpg"   width="160"/> | <img src="./assets/nearmiss_depth.jpg"   width="160"/> | <img src="./assets/nearmiss_segmentation.jpg"   width="160"/> | <img src="./assets/nearmiss_shaded_seg.jpg"   width="160"/> | <img src="./assets/nearmiss_edges.jpg"   width="160"/> |
+| Fire         | <img src="./assets/scenario_fire.jpg"       width="160"/> | <img src="./assets/fire_depth.jpg"       width="160"/> | <img src="./assets/fire_segmentation.jpg"       width="160"/> | <img src="./assets/fire_shaded_seg.jpg"       width="160"/> | <img src="./assets/fire_edges.jpg"       width="160"/> |
+| Collision    | <img src="./assets/scenario_collision.jpg"  width="160"/> | <img src="./assets/collision_depth.jpg"  width="160"/> | <img src="./assets/collision_segmentation.jpg"  width="160"/> | <img src="./assets/collision_shaded_seg.jpg"  width="160"/> | <img src="./assets/collision_edges.jpg"  width="160"/> |
+| Box pickup   | <img src="./assets/scenario_box_pickup.jpg" width="160"/> | <img src="./assets/box_pickup_depth.jpg" width="160"/> | <img src="./assets/box_pickup_segmentation.jpg" width="160"/> | <img src="./assets/box_pickup_shaded_seg.jpg" width="160"/> | <img src="./assets/box_pickup_edges.jpg" width="160"/> |
+
+---
+
+## Dataset statistics
+
+| Scenario                     | # clips    | # runs (samples) | Clip length | Cameras / run | Repo path |
+|------------------------------|-----------:|-----------------:|------------:|--------------:|---|
+| 🚜 Forklift–human near-miss  | 27,939     | 13,410 *(¹)*     | 10 s        | 10 / 1        | `rgb/forklift_human_nearmiss/` |
+| 🔥 Warehouse fire            | 44,734     | 9,064            | 10 s        | 5             | `rgb/warehouse_fire/` |
+| 💥 Forklift–shelf collision  | 24,617     | 4,120            | 15 s        | 6             | `rgb/forklift_shelf_collision/` |
+| 📦 Warehouse box pickup      | 25,677     | 2,601            | 15 s        | 10            | `rgb/warehouse_box_pickup/` |
+| **Total**                    | **122,967**| **29,195**       | —           | —             | — |
+
+Aggregate footage: **≈ 412 hours** at 1920 × 1080 / 30 fps. The "samples" column is the number of `__key__` entries you'll see when iterating with WebDataset (i.e. tar groups, not individual mp4 entries).
+
+> *(¹)* **Nearmiss composition.** Nearmiss is a **union of two source pipelines**:
+> - **1,642 multi-camera runs** from the IRA/IRO multi-cam pipeline (10 cameras each → 16,171 clips, with `source_kind = "multi_camera_run"` in `meta.json`).
+> - **11,768 single-camera clips** legacy-merged from earlier `warehouse_5k_videos/` and `warehouse_6k_videos/` training-set dumps (one camera each, packed as 1-camera samples with `source_kind = "extras_single_view"`, no per-run scene composition metadata). These are listed at the END of the nearmiss shard range (`nearmiss-rgb-00051.tar` onward).
+>
+> Filter by `meta["source_kind"]` or by `clips.parquet["source_kind"]` to select one or both.
+
+---
+
+## Simulation infrastructure
+
+All four scenarios are built on **NVIDIA Isaac Sim**:
+
+- **Procedural scene composition** — warehouse layout, shelf placement, prop variation, and per-light randomization of color temperature, intensity, exposure, and color — handled by **Isaac Sim Replicator Object (IRO)**.
+- **Agent and sensor population** — worker spawning and behavior, forklift placement and navigation, and the camera rigs that define the dataset's multi-view viewpoints — handled by **Isaac Sim Replicator Agent (IRA)**.
+- **Camera placement** is parametric, with height, distance, and look-down angle sampled per run.
+- **Worker assets and motions** are sampled from Isaac Sim's character library to diversify human appearance and gait.
+- Each simulation run is **seeded with a unique random seed** (exposed as `meta.seed` per sample) that controls all randomized variables (scene composition, lighting, agent identity and motion, camera pose, event timing), so runs are independent and reproducible.
+
+---
+
+## Repository layout
+
+```
+nvidia/PhysicalAI-SDG-WareHouse/
+├── README.md
+├── metadata/
+│   ├── runs.parquet           # 1 row per run/sample, with clip_keys[], shard_path, etc.
+│   ├── clips.parquet          # 1 row per (run × camera), with scenario, seed, hash_filename, …
+│   └── manifests/             # provenance copies of the source-S3 manifests
+├── rgb/                       # ← this release
+│   ├── forklift_human_nearmiss/
+│   │   ├── nearmiss-rgb-00000.tar
+│   │   ├── nearmiss-rgb-00001.tar
+│   │   └── … (113 shards, ~5 GiB each)
+│   ├── warehouse_fire/        # 125 shards
+│   ├── forklift_shelf_collision/   # 114 shards
+│   └── warehouse_box_pickup/  # 107 shards
+└── artifacts/                 # ← coming soon: depth/seg/edges + bboxes + camera params
+```
+
+Each `.tar` is a **WebDataset** archive. Inside it, every sample (= one simulation run = one set of synchronized cameras) is a group of entries sharing the same stem (`run_id`):
+
+```
+fd7cc35596b247b16b0b_run_8_seed_864110064.meta.json
+fd7cc35596b247b16b0b_run_8_seed_864110064.metadata.txt
+fd7cc35596b247b16b0b_run_8_seed_864110064.ceiling_00.rgb.mp4
+fd7cc35596b247b16b0b_run_8_seed_864110064.ceiling_01.rgb.mp4
+fd7cc35596b247b16b0b_run_8_seed_864110064.ceiling_02.rgb.mp4
+fd7cc35596b247b16b0b_run_8_seed_864110064.ceiling_03.rgb.mp4
+fd7cc35596b247b16b0b_run_8_seed_864110064.ceiling_04.rgb.mp4
+fd7cc35596b247b16b0b_run_8_seed_864110064.eye_00.rgb.mp4
+…
+```
+
+WebDataset readers will yield one Python dict per `run_id` with `__key__ = run_id` and keys `meta.json`, `metadata.txt` (when present), and one `{alias}.rgb.mp4` per camera.
+
+---
+
+## Loading examples
+
+### Stream from HF without downloading shards — WebDataset
+
+```python
+import os, webdataset as wds
+from huggingface_hub import get_token
+
+token = get_token() or os.environ["HF_TOKEN"]
+url = (
+    "pipe:curl -s -L "
+    "'https://huggingface.co/datasets/nvidia/PhysicalAI-SDG-WareHouse/resolve/main"
+    "/rgb/warehouse_fire/fire-rgb-{00000..00124}.tar' "
+    f"-H 'Authorization: Bearer {token}'"
+)
+
+ds = wds.WebDataset(url, shardshuffle=True).decode()
+for sample in ds.shuffle(1000):
+    run_id = sample["__key__"]
+    meta   = sample["meta.json"]          # dict
+    # one bytes blob per camera:
+    rgb    = sample["ceiling_00.rgb.mp4"] # bytes, decode with av/ffmpeg
+    print(run_id, meta["seed"], list(k for k in sample if k.endswith(".rgb.mp4")))
+    break
+```
+
+### Filter with Parquet, then download just the shards you need
+
+```python
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+clips = pd.read_parquet(
+    hf_hub_download(
+        repo_id="nvidia/PhysicalAI-SDG-WareHouse",
+        repo_type="dataset",
+        filename="metadata/clips.parquet",
+    )
+)
+
+# All eye-level views from fire runs with even seed:
+sel = clips[
+    (clips.scenario == "fire")
+    & (clips.camera_alias.str.startswith("eye_"))
+    & (clips.seed.notna()) & (clips.seed % 2 == 0)
+]
+print(sel.shape, sel.shard_path_in_repo.nunique(), "unique shards to fetch")
+```
+
+### Pull one scenario only with the CLI
+
+```bash
+huggingface-cli download nvidia/PhysicalAI-SDG-WareHouse \
+  --repo-type dataset \
+  --include "rgb/warehouse_box_pickup/**" "metadata/**" \
+  --local-dir ./PhysicalAI-SDG-WareHouse
+```
+
+### Pull a single shard programmatically
+
+```python
+from huggingface_hub import hf_hub_download
+local_tar = hf_hub_download(
+    repo_id="nvidia/PhysicalAI-SDG-WareHouse",
+    repo_type="dataset",
+    filename="rgb/forklift_human_nearmiss/nearmiss-rgb-00000.tar",
+)
+```
+
+---
+
+## Dataset description
+
+| Field | Value |
+|---|---|
+| **Owner** | NVIDIA |
+| **Creation date** | 2026 |
+| **Data collection method** | Synthetic (NVIDIA Isaac Sim, IRO + IRA) |
+| **Labeling method** | Automatic (Isaac Sim Replicator) |
+| **Container / codec** | MP4 (H.264) |
+| **Resolution** | 1920 × 1080 |
+| **Frame rate** | 30 FPS |
+| **Packaging** | WebDataset (tar shards, ~5 GiB each) |
+| **Language(s) of metadata** | English |
+| **License** | CC BY 4.0 |
+
+---
+
+## Known limitations
+
+- **v1 = RGB only.** The annotation stack (depth, instance + shaded segmentation, Canny edges, 2D/3D bounding boxes, per-frame camera intrinsics+extrinsics) is rendered and aligned to every RGB frame but ships in an upcoming `artifacts/` tier of this same repo. The visualization gallery above shows those native renders for reference.
+- **Heterogeneous nearmiss composition.** Nearmiss combines 1,642 multi-camera runs (10 cameras each) with 11,768 single-camera legacy clips from older training-set dumps; the latter lack run-level scene-composition metadata and live in shards `nearmiss-rgb-00051..00112`. Use `meta["source_kind"]` to disambiguate.
+- **Sim-to-real gap.** SDG-Warehouse is fully synthetic and may exhibit appearance differences from real warehouse footage, including a CG-like look, simplified material response, and limited fidelity in volumetric effects such as smoke and fire.
+- **Stylized agent motion.** Worker motion is driven by procedural navigation and behavior, so reactions — particularly emergency evacuation in the fire scenario — can occasionally appear unnatural.
+- **Approximate fine-grained physics.** Rigid-body interactions (forklift contact with shelves and props) are physically simulated, but very fine-grained debris, deformation, and secondary contact effects are approximate.
+- **Environment diversity.** The release is concentrated on a single warehouse layout family; future work will add additional warehouse, retail, and factory floor plans.
+- **Incident coverage.** Future work will add event types such as spills, dropped pallets, and shelf collapses without forklift involvement.
+- **Human-attribute diversity.** Future work will broaden variation in worker attire and personal protective equipment (PPE).
+
+---
+
+## Contributors
+
+Nalin Dadhich, Jiajun Li, Robert Denomme, Prahan Reddy Poreddy, Patrick Kim.
+
+---
+
+## Citation
+
+If you use SDG-Warehouse in your research, please cite the Cosmos3 technical report:
+
+```bibtex
+@techreport{nvidia2026cosmos3,
+  title       = {Cosmos3: World Foundation Models for Perception, Reasoning, Simulation, and Action},
+  author      = {{NVIDIA}},
+  year        = {2026},
+  institution = {NVIDIA},
+  note        = {See Appendix A.1.5 (SDG-Warehouse)}
+}
+```
+
+---
+
+## License
+
+Released under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license.
+
+---
+
+## Ethical considerations
+
+NVIDIA believes Trustworthy AI is a shared responsibility, and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this dataset meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
+
+SDG-Warehouse is **fully synthetic** — no real people, real workplaces, or real surveillance footage are included — and depicts safety-critical events (near-miss, collision, fire, evacuation) only in simulation. Models trained on it should still be carefully evaluated on real footage before being deployed in any safety-critical setting, and operators should be aware of the sim-to-real gap noted above.
+
+Please report security vulnerabilities or NVIDIA AI concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).