diff --git a/src/lerobot/scripts/lerobot_dataset_viz.py b/src/lerobot/scripts/lerobot_dataset_viz.py
index 535b84e77..66ac5a511 100644
--- a/src/lerobot/scripts/lerobot_dataset_viz.py
+++ b/src/lerobot/scripts/lerobot_dataset_viz.py
@@ -92,8 +92,7 @@ def get_feature_names(dataset: LeRobotDataset, key: str) -> list[str]:
 
 
 def get_sequential_colors(num_dims: int) -> list[list[int]]:
-    """Return a deterministic list of distinct RGB colors, one per dimension.
-    """
+    """Return a deterministic list of distinct RGB colors, one per dimension."""
     colors = []
     for d in range(num_dims):
         hue = d / max(num_dims, 1)
@@ -111,6 +110,41 @@ def to_hwc_uint8_numpy(chw_float32_torch: torch.Tensor) -> np.ndarray:
     return hwc_uint8_numpy
 
 
+def build_blueprint_from_dataset(dataset: LeRobotDataset):
+    """Build a Rerun blueprint laying out camera images and time series for the given dataset.
+
+    Camera images are arranged in a grid on the left, and the available scalar signals
+    (action, state, reward, done, success) are stacked as time series views on the right.
+    The per-dimension series names and colors for ``action`` and ``state`` are applied
+    directly via blueprint overrides.
+    """
+    import rerun as rr
+    import rerun.blueprint as rrb
+
+    image_views = [rrb.Spatial2DView(origin=key, name=key) for key in dataset.meta.camera_keys]
+
+    timeseries_views = []
+    # Style multi-dimensional signals (action, state) with per-dimension names and colors.
+    for origin, key in ((ACTION, ACTION), ("state", OBS_STATE)):
+        if key in dataset.features:
+            names = get_feature_names(dataset, key)
+            styling = rr.SeriesLines(names=names, colors=get_sequential_colors(len(names)))
+            timeseries_views.append(
+                rrb.TimeSeriesView(origin=origin, name=origin, overrides={origin: styling})
+            )
+    for key in (DONE, REWARD, "next.success"):
+        if key in dataset.features:
+            timeseries_views.append(rrb.TimeSeriesView(origin=key, name=key))
+
+    contents = []
+    if image_views:
+        contents.append(rrb.Grid(*image_views, name="images"))
+    if timeseries_views:
+        contents.append(rrb.Vertical(*timeseries_views, name="time series"))
+
+    return rrb.Blueprint(rrb.Horizontal(*contents) if contents else rrb.Grid())
+
+
 def visualize_dataset(
     dataset: LeRobotDataset,
     episode_index: int,
@@ -149,7 +183,8 @@ def visualize_dataset(
     import rerun as rr
 
     spawn_local_viewer = mode == "local" and not save
-    rr.init(f"{repo_id}/episode_{episode_index}", spawn=spawn_local_viewer)
+    blueprint = build_blueprint_from_dataset(dataset)
+    rr.init(f"{repo_id}/episode_{episode_index}", spawn=spawn_local_viewer, default_blueprint=blueprint)
 
     # Manually call python garbage collector after `rr.init` to avoid hanging in a blocking flush
     # when iterating on a dataloader with `num_workers` > 0
@@ -163,36 +198,23 @@ def visualize_dataset(
 
     logging.info("Logging to Rerun")
 
-    # Name each series once (static) so all dimensions share a single view while keeping labels.
-    # Labels come straight from the dataset metadata.
-    if ACTION in dataset.features:
-        names = get_feature_names(dataset, ACTION)
-        rr.log(ACTION, rr.SeriesLines(names=names, colors=get_sequential_colors(len(names))), static=True)
-    if OBS_STATE in dataset.features:
-        names = get_feature_names(dataset, OBS_STATE)
-        rr.log("state", rr.SeriesLines(names=names, colors=get_sequential_colors(len(names))), static=True)
-
     first_index = None
     for batch in tqdm.tqdm(dataloader, total=len(dataloader)):
         if first_index is None:
             first_index = batch["index"][0].item()
 
-        # iterate over the batch
         for i in range(len(batch["index"])):
             rr.set_time("frame_index", sequence=batch["index"][i].item() - first_index)
             rr.set_time("timestamp", timestamp=batch["timestamp"][i].item())
 
-            # display each camera image
             for key in dataset.meta.camera_keys:
                 img = to_hwc_uint8_numpy(batch[key][i])
                 img_entity = rr.Image(img).compress() if display_compressed_images else rr.Image(img)
                 rr.log(key, entity=img_entity)
 
-            # display each dimension of action space (e.g. actuators command)
             if ACTION in batch:
                 rr.log(ACTION, rr.Scalars(batch[ACTION][i].numpy()))
 
-            # display each dimension of observed state space (e.g. agent position in joint space)
             if OBS_STATE in batch:
                 rr.log("state", rr.Scalars(batch[OBS_STATE][i].numpy()))
 
@@ -206,8 +228,6 @@ def visualize_dataset(
                 rr.log("next.success", rr.Scalars(batch["next.success"][i].item()))
 
     if mode == "local" and save:
-        # save .rrd locally
-        output_dir = Path(output_dir)
         output_dir.mkdir(parents=True, exist_ok=True)
         repo_id_str = repo_id.replace("/", "_")
         rrd_path = output_dir / f"{repo_id_str}_episode_{episode_index}.rrd"
@@ -215,7 +235,7 @@ def visualize_dataset(
         return rrd_path
 
     elif mode == "distant":
-        # stop the process from exiting since it is serving the websocket connection
+        # Keep the process alive while it serves the gRPC/web connection.
         try:
             while True:
                 time.sleep(1)
@@ -335,7 +355,7 @@ def main():
     logging.info("Loading dataset")
     dataset = LeRobotDataset(repo_id, episodes=[args.episode_index], root=root, tolerance_s=tolerance_s)
 
-    visualize_dataset(dataset, **vars(args))
+    visualize_dataset(dataset, **kwargs)
 
 
 if __name__ == "__main__":