Upload README.md with huggingface_hub

README.md

@@ -87,10 +87,10 @@ Pulling a single scenario (RGB only) is the recommended starting point for most
 ```bash
 pip install -U "huggingface_hub[hf_xet]"
 
-huggingface-cli download nvidia/PhysicalAI-SDG-WareHouse \
+huggingface-cli download nvidia/PhysicalAI-WorldModel-Synthetic-Warehouse-Operations-Scenes \
   --repo-type dataset \
   --include "rgb/forklift_human_nearmiss/**" "metadata/**" \
-  --local-dir ./PhysicalAI-SDG-WareHouse
+  --local-dir ./PhysicalAI-WorldModel-Synthetic-Warehouse-Operations-Scenes
 ```
 
 The RGB and artifact tiers per scenario are summarized below.
@@ -178,7 +178,7 @@ Each simulation run is seeded with a unique random seed that controls every rand
 ## Repository layout
 
 ```
-nvidia/PhysicalAI-SDG-WareHouse/
+nvidia/PhysicalAI-WorldModel-Synthetic-Warehouse-Operations-Scenes/
 ├── README.md
 ├── assets/                     ← images used by this dataset card
 ├── metadata/
@@ -244,7 +244,7 @@ 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"
+    "'https://huggingface.co/datasets/nvidia/PhysicalAI-WorldModel-Synthetic-Warehouse-Operations-Scenes/resolve/main"
     "/rgb/warehouse_fire/fire-rgb-{00000..00124}.tar' "
     f"-H 'Authorization: Bearer {token}'"
 )
@@ -269,7 +269,7 @@ from huggingface_hub import hf_hub_download
 
 clips = pd.read_parquet(
     hf_hub_download(
-        repo_id="nvidia/PhysicalAI-SDG-WareHouse",
+        repo_id="nvidia/PhysicalAI-WorldModel-Synthetic-Warehouse-Operations-Scenes",
         repo_type="dataset",
         filename="metadata/clips.parquet",
     )
@@ -293,21 +293,21 @@ print(f"{len(selection):,} clips across {len(unique_shards)} shards")
 To pull RGB only:
 
 ```bash
-huggingface-cli download nvidia/PhysicalAI-SDG-WareHouse \
+huggingface-cli download nvidia/PhysicalAI-WorldModel-Synthetic-Warehouse-Operations-Scenes \
   --repo-type dataset \
   --include "rgb/warehouse_box_pickup/**" "metadata/**" \
-  --local-dir ./PhysicalAI-SDG-WareHouse
+  --local-dir ./PhysicalAI-WorldModel-Synthetic-Warehouse-Operations-Scenes
 ```
 
 To pull RGB plus the matching artifacts (depth, segmentation, shaded segmentation, canny edges, instance-id NPZ, and per-frame camera and bounding-box JSONL):
 
 ```bash
-huggingface-cli download nvidia/PhysicalAI-SDG-WareHouse \
+huggingface-cli download nvidia/PhysicalAI-WorldModel-Synthetic-Warehouse-Operations-Scenes \
   --repo-type dataset \
   --include "rgb/warehouse_box_pickup/**" \
             "artifacts/warehouse_box_pickup/**" \
             "metadata/**" \
-  --local-dir ./PhysicalAI-SDG-WareHouse
+  --local-dir ./PhysicalAI-WorldModel-Synthetic-Warehouse-Operations-Scenes
 ```
 
 ### Pull a single shard programmatically
@@ -316,12 +316,12 @@ huggingface-cli download nvidia/PhysicalAI-SDG-WareHouse \
 from huggingface_hub import hf_hub_download
 
 local_rgb = hf_hub_download(
-    repo_id="nvidia/PhysicalAI-SDG-WareHouse",
+    repo_id="nvidia/PhysicalAI-WorldModel-Synthetic-Warehouse-Operations-Scenes",
     repo_type="dataset",
     filename="rgb/forklift_human_nearmiss/nearmiss-rgb-00000.tar",
 )
 local_artifacts = hf_hub_download(
-    repo_id="nvidia/PhysicalAI-SDG-WareHouse",
+    repo_id="nvidia/PhysicalAI-WorldModel-Synthetic-Warehouse-Operations-Scenes",
     repo_type="dataset",
     filename="artifacts/forklift_human_nearmiss/nearmiss-artifacts-00000.tar",
 )
@@ -345,7 +345,7 @@ If you use SDG-Warehouse in your research, please cite the dataset itself as wel
   author       = {Dadhich, Nalin},
   year         = {2026},
   publisher    = {NVIDIA Corporation},
-  howpublished = {\url{https://huggingface.co/datasets/nvidia/PhysicalAI-SDG-WareHouse}}
+  howpublished = {\url{https://huggingface.co/datasets/nvidia/PhysicalAI-WorldModel-Synthetic-Warehouse-Operations-Scenes}}
 }
 
 @techreport{nvidia2026cosmos3,