Expand data loading section with datasets and PyTorch examples

README.md

@@ -329,19 +329,69 @@ This dataset is intended for embodied AI and egocentric robotics research.
 - object tracks are only present when source frames include object detections
 - this export is optimized for structured ML ingestion rather than human-readable storytelling
 
-## Data Loading Example
+## Data Loading
+
+### Clip metadata with the datasets library
+
+The clip-level metadata is exposed as a loadable config with `train`, `validation` and `test` splits. This dataset is gated, so authenticate first with `hf auth login` (or pass `token=True`).
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("RoboXTechnologies/RoboX-EgoTask", "clips")
+print(ds)
+
+row = ds["train"][0]
+print(row["clip_id"], row["task_category"], row["narration"])
+print(row["video_url"])     # clean RGB clip
+print(row["overlay_url"])   # same clip with hand landmarks rendered
+```
+
+### Full per-frame annotations from a local copy
+
+The config above is a flat summary, one row per clip. The complete per-frame streams (hand keypoints, sensors, trajectory and more) ship as JSONL under `annotations/` and `metadata/`. Download or clone the repo, then read them directly:
 
 ```python
 import json
 from pathlib import Path
 
-root = Path("RoboX-EgoTask")
+root = Path("RoboX-EgoTask")  # path to the downloaded repo
 clips = [json.loads(line) for line in (root / "metadata" / "clips.jsonl").read_text().splitlines()]
-# Filter clips that have hand keypoints in this export tier
 hand_clips = [c for c in clips if (c.get("exported_modalities") or {}).get("hand_keypoints_2d")]
 print(clips[0]["clip_id"], clips[0]["labels"])
 ```
 
+### Streaming clip videos into PyTorch
+
+A minimal `Dataset` that pulls each clip's MP4 on demand (cached after the first fetch) and decodes it to a tensor. Clips vary in length, so this uses `batch_size=1`; add a `collate_fn` that pads or samples a fixed number of frames to batch them.
+
+```python
+import torch
+from torch.utils.data import Dataset, DataLoader
+from datasets import load_dataset
+from huggingface_hub import hf_hub_download
+import torchvision
+
+REPO = "RoboXTechnologies/RoboX-EgoTask"
+
+class EgoTaskClips(Dataset):
+    def __init__(self, split="train"):
+        self.rows = load_dataset(REPO, "clips", split=split)
+
+    def __len__(self):
+        return len(self.rows)
+
+    def __getitem__(self, idx):
+        row = self.rows[idx]
+        path = hf_hub_download(REPO, f"clips/{row['clip_id']}.mp4", repo_type="dataset")
+        video, _, _ = torchvision.io.read_video(path, output_format="TCHW")  # (T, C, H, W)
+        return {"clip_id": row["clip_id"], "video": video, "label": row["task_category"]}
+
+loader = DataLoader(EgoTaskClips("train"), batch_size=1, shuffle=True)
+batch = next(iter(loader))
+print(batch["clip_id"], batch["video"].shape)
+```
+
 ## Citation
 
 ```bibtex