diff --git a/src/lerobot/scripts/lerobot_edit_dataset.py b/src/lerobot/scripts/lerobot_edit_dataset.py
index 83ba027bcc..e9b6fd5786 100644
--- a/src/lerobot/scripts/lerobot_edit_dataset.py
+++ b/src/lerobot/scripts/lerobot_edit_dataset.py
@@ -18,7 +18,7 @@
 Edit LeRobot datasets using various transformation tools.
 
 This script allows you to delete episodes, split datasets, merge datasets,
-and remove features. When new_repo_id is specified, creates a new dataset.
+remove features, and add features. When new_repo_id is specified, creates a new dataset.
 
 Usage Examples:
 
@@ -65,6 +65,12 @@
         --operation.type remove_feature \
         --operation.feature_names "['observation.images.top']"
 
+Add feature from numpy file:
+    python -m lerobot.scripts.lerobot_edit_dataset \
+        --repo_id lerobot/pusht \
+        --operation.type add_feature \
+        --operation.features '{"reward": {"file": "rewards.npy", "dtype": "float32", "shape": [1], "names": null}}'
+
 Using JSON config file:
     python -m lerobot.scripts.lerobot_edit_dataset \
         --config_path path/to/edit_config.json
@@ -75,8 +81,11 @@
 from dataclasses import dataclass
 from pathlib import Path
 
+import numpy as np
+
 from lerobot.configs import parser
 from lerobot.datasets.dataset_tools import (
+    add_features,
     delete_episodes,
     merge_datasets,
     remove_feature,
@@ -111,10 +120,16 @@ class RemoveFeatureConfig:
     feature_names: list[str] | None = None
 
 
+@dataclass
+class AddFeatureConfig:
+    type: str = "add_feature"
+    features: dict[str, dict] | None = None
+
+
 @dataclass
 class EditDatasetConfig:
     repo_id: str
-    operation: DeleteEpisodesConfig | SplitConfig | MergeConfig | RemoveFeatureConfig
+    operation: DeleteEpisodesConfig | SplitConfig | MergeConfig | RemoveFeatureConfig | AddFeatureConfig
     root: str | None = None
     new_repo_id: str | None = None
     push_to_hub: bool = False
@@ -258,6 +273,90 @@ def handle_remove_feature(cfg: EditDatasetConfig) -> None:
         LeRobotDataset(output_repo_id, root=output_dir).push_to_hub()
 
 
+def handle_add_feature(cfg: EditDatasetConfig) -> None:
+    if not isinstance(cfg.operation, AddFeatureConfig):
+        raise ValueError("Operation config must be AddFeatureConfig")
+
+    if not cfg.operation.features:
+        raise ValueError("features must be specified for add_feature operation")
+
+    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
+    output_repo_id, output_dir = get_output_path(
+        cfg.repo_id, cfg.new_repo_id, Path(cfg.root) if cfg.root else None
+    )
+
+    if cfg.new_repo_id is None:
+        dataset.root = Path(str(dataset.root) + "_old")
+
+    # Process features config to load data and prepare for add_features
+    features_dict = {}
+    for feature_name, feature_config in cfg.operation.features.items():
+        # Extract feature info (dtype, shape, names)
+        shape = feature_config.get("shape")
+        dtype = feature_config.get("dtype")
+        # Convert and validate shape before assignment
+        if isinstance(shape, list):
+            shape = tuple(shape)
+        elif isinstance(shape, tuple) or shape is None:
+            pass  # shape is already valid
+        else:
+            raise ValueError(
+                f"Feature '{feature_name}' has invalid shape type: {type(shape).__name__}. "
+                "Shape must be a list, tuple, or None."
+            )
+        # Validate required metadata fields
+        if dtype is None:
+            raise ValueError(f"Feature '{feature_name}' must specify a 'dtype' (data type)")
+        if shape is None:
+            raise ValueError(f"Feature '{feature_name}' must specify a 'shape'")
+
+        feature_info = {
+            "dtype": dtype,
+            "shape": shape,
+            "names": feature_config.get("names"),
+        }
+
+        # Load feature data from file
+        feature_file = feature_config.get("file")
+        if not feature_file:
+            raise ValueError(f"Feature '{feature_name}' must specify a 'file' path to load data from")
+
+        file_path = Path(feature_file)
+        if not file_path.exists():
+            raise FileNotFoundError(f"Feature file not found: {feature_file}")
+
+        # Load numpy array
+        if file_path.suffix == ".npy":
+            feature_data = np.load(file_path)
+        else:
+            raise ValueError(f"Unsupported file format for feature '{feature_name}': {file_path.suffix}")
+
+        # Validate data length matches dataset
+        expected_length = dataset.meta.total_frames
+        if len(feature_data) != expected_length:
+            raise ValueError(
+                f"Feature '{feature_name}' data length ({len(feature_data)}) "
+                f"does not match dataset length ({expected_length})"
+            )
+
+        features_dict[feature_name] = (feature_data, feature_info)
+
+    logging.info(f"Adding features {list(features_dict.keys())} to {cfg.repo_id}")
+    new_dataset = add_features(
+        dataset,
+        features=features_dict,
+        output_dir=output_dir,
+        repo_id=output_repo_id,
+    )
+
+    logging.info(f"Dataset saved to {output_dir}")
+    logging.info(f"Updated features: {list(new_dataset.meta.features.keys())}")
+
+    if cfg.push_to_hub:
+        logging.info(f"Pushing to hub as {output_repo_id}")
+        LeRobotDataset(output_repo_id, root=output_dir).push_to_hub()
+
+
 @parser.wrap()
 def edit_dataset(cfg: EditDatasetConfig) -> None:
     operation_type = cfg.operation.type
@@ -270,10 +369,12 @@ def edit_dataset(cfg: EditDatasetConfig) -> None:
         handle_merge(cfg)
     elif operation_type == "remove_feature":
         handle_remove_feature(cfg)
+    elif operation_type == "add_feature":
+        handle_add_feature(cfg)
     else:
         raise ValueError(
             f"Unknown operation type: {operation_type}\n"
-            f"Available operations: delete_episodes, split, merge, remove_feature"
+            f"Available operations: delete_episodes, split, merge, remove_feature, add_feature"
         )