Add docs for LeRobot Image transforms (#1972)

* Remove unused scripts, add docs for image transforms and add example

* fix(examples): move train_policy.py under examples, remove outdated readme parts

* remove script thats copied to train folder

* remove outdated links to examples and example tests

docs/source/lerobot-dataset-v3.mdx

@@ -8,6 +8,7 @@ This docs will guide you to:
 - Record a dataset and push it to the Hub
 - Load datasets for training with `LeRobotDataset`
 - Stream datasets without downloading using `StreamingLeRobotDataset`
+- Apply image transforms for data augmentation during training
 - Migrate existing `v2.1` datasets to `v3.0`
 
 ## What’s new in `v3`
@@ -150,6 +151,117 @@ dataset = StreamingLeRobotDataset(repo_id)  # streams directly from the Hub
   </figure>
 </div>
 
+## Image transforms
+
+Image transforms are data augmentations applied to camera frames during training to improve model robustness and generalization. LeRobot supports various transforms including brightness, contrast, saturation, hue, and sharpness adjustments.
+
+### Using transforms during dataset creation/recording
+
+Currently, transforms are applied during **training time only**, not during recording. When you create or record a dataset, the raw images are stored without transforms. This allows you to experiment with different augmentations later without re-recording data.
+
+### Adding transforms to existing datasets (API)
+
+Use the `image_transforms` parameter when loading a dataset for training:
+
+```python
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.transforms import ImageTransforms, ImageTransformsConfig, ImageTransformConfig
+
+# Option 1: Use default transform configuration (disabled by default)
+transforms_config = ImageTransformsConfig(
+    enable=True,  # Enable transforms
+    max_num_transforms=3,  # Apply up to 3 transforms per frame
+    random_order=False,  # Apply in standard order
+)
+transforms = ImageTransforms(transforms_config)
+
+dataset = LeRobotDataset(
+    repo_id="your-username/your-dataset",
+    image_transforms=transforms
+)
+
+# Option 2: Create custom transform configuration
+custom_transforms_config = ImageTransformsConfig(
+    enable=True,
+    max_num_transforms=2,
+    random_order=True,
+    tfs={
+        "brightness": ImageTransformConfig(
+            weight=1.0,
+            type="ColorJitter",
+            kwargs={"brightness": (0.7, 1.3)}  # Adjust brightness range
+        ),
+        "contrast": ImageTransformConfig(
+            weight=2.0,  # Higher weight = more likely to be selected
+            type="ColorJitter",
+            kwargs={"contrast": (0.8, 1.2)}
+        ),
+        "sharpness": ImageTransformConfig(
+            weight=0.5,  # Lower weight = less likely to be selected
+            type="SharpnessJitter",
+            kwargs={"sharpness": (0.3, 2.0)}
+        ),
+    }
+)
+
+dataset = LeRobotDataset(
+    repo_id="your-username/your-dataset",
+    image_transforms=ImageTransforms(custom_transforms_config)
+)
+
+# Option 3: Use pure torchvision transforms
+from torchvision.transforms import v2
+
+torchvision_transforms = v2.Compose([
+    v2.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),
+    v2.GaussianBlur(kernel_size=3, sigma=(0.1, 2.0)),
+])
+
+dataset = LeRobotDataset(
+    repo_id="your-username/your-dataset",
+    image_transforms=torchvision_transforms
+)
+```
+
+### Available transform types
+
+LeRobot provides several transform types:
+
+- **`ColorJitter`**: Adjusts brightness, contrast, saturation, and hue
+- **`SharpnessJitter`**: Randomly adjusts image sharpness
+- **`Identity`**: No transformation (useful for testing)
+
+You can also use any `torchvision.transforms.v2` transform by passing it directly to the `image_transforms` parameter.
+
+### Configuration options
+
+- **`enable`**: Enable/disable transforms (default: `False`)
+- **`max_num_transforms`**: Maximum number of transforms applied per frame (default: `3`)
+- **`random_order`**: Apply transforms in random order vs. standard order (default: `False`)
+- **`weight`**: Sampling probability for each transform (higher = more likely, if sum of weights is not 1, they will be normalized)
+- **`kwargs`**: Transform-specific parameters (e.g., brightness range)
+
+### Visualizing transforms
+
+Use the visualization script to preview how transforms affect your data:
+
+```bash
+python -m lerobot.scripts.visualize_image_transforms \
+  --repo-id=your-username/your-dataset \
+  --output-dir=./transform_examples \
+  --n-examples=5
+```
+
+This saves example images showing the effect of each transform, helping you tune parameters.
+
+### Best practices
+
+- **Start conservative**: Begin with small ranges (e.g., brightness 0.9-1.1) and increase gradually
+- **Test first**: Use the visualization script to ensure transforms look reasonable
+- **Monitor training**: Strong augmentations can hurt performance if too aggressive
+- **Match your domain**: If your robot operates in varying lighting, use brightness/contrast transforms
+- **Combine wisely**: Using too many transforms simultaneously can make training unstable
+
 ## Migrate `v2.1` → `v3.0`
 
 A converter aggregates per‑episode files into larger shards and writes episode offsets/metadata. Convert your dataset using the instructions below.