From b3d74f80f042a137b5ba46cf977e85a4da0e6ad3 Mon Sep 17 00:00:00 2001
From: Khalil Meftah <khalil.meftah@huggingface.co>
Date: Fri, 19 Jun 2026 18:31:12 +0200
Subject: [PATCH] Fix batch wandb logging metrics and handle scalar stats
 (#3821)

* fix(logging): batch wandb metrics

- Batch all metrics into a single wandb.log() call instead of one per
key, reducing API overhead.

- Add support for list-valued metrics by expanding them to indexed keys (e.g.
metric_0, metric_1).

* fix(stats): handle scalar stats robustly

- Wrap cast_stats_to_numpy with np.atleast_1d to prevent 0-d arrays
from scalar stats causing shape mismatches downstream.

* fix(logging): remove unused list-valued metric expansion

---------

Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
---
 src/lerobot/common/wandb_utils.py | 20 +++++++++++---------
 src/lerobot/datasets/io_utils.py  |  2 +-
 2 files changed, 12 insertions(+), 10 deletions(-)

diff --git a/src/lerobot/common/wandb_utils.py b/src/lerobot/common/wandb_utils.py
index b782cd751..c229b5eaa 100644
--- a/src/lerobot/common/wandb_utils.py
+++ b/src/lerobot/common/wandb_utils.py
@@ -180,24 +180,26 @@ class WandBLogger:
                 self._wandb_custom_step_key.add(new_custom_key)
                 self._wandb.define_metric(new_custom_key, hidden=True)
 
+        batch_data = {}
         for k, v in d.items():
+            # Skip the custom step key here, it's added to the batch below.
+            if custom_step_key is not None and k == custom_step_key:
+                continue
+
             if not isinstance(v, (int | float | str)):
                 logging.warning(
                     f'WandB logging of key "{k}" was ignored as its type "{type(v)}" is not handled by this wrapper.'
                 )
                 continue
 
-            # Do not log the custom step key itself.
-            if self._wandb_custom_step_key is not None and k in self._wandb_custom_step_key:
-                continue
+            batch_data[f"{mode}/{k}"] = v
 
+        if batch_data:
             if custom_step_key is not None:
-                value_custom_step = d[custom_step_key]
-                data = {f"{mode}/{k}": v, f"{mode}/{custom_step_key}": value_custom_step}
-                self._wandb.log(data)
-                continue
-
-            self._wandb.log(data={f"{mode}/{k}": v}, step=step)
+                batch_data[f"{mode}/{custom_step_key}"] = d[custom_step_key]
+                self._wandb.log(batch_data)
+            else:
+                self._wandb.log(data=batch_data, step=step)
 
     def log_video(self, video_path: str, step: int, mode: str = "train"):
         if mode not in {"train", "eval"}:
diff --git a/src/lerobot/datasets/io_utils.py b/src/lerobot/datasets/io_utils.py
index b6344942c..be94f3b3a 100644
--- a/src/lerobot/datasets/io_utils.py
+++ b/src/lerobot/datasets/io_utils.py
@@ -154,7 +154,7 @@ def cast_stats_to_numpy(stats: dict) -> dict[str, dict[str, np.ndarray]]:
     Returns:
         dict: The statistics dictionary with values cast to numpy arrays.
     """
-    stats = {key: np.array(value) for key, value in flatten_dict(stats).items()}
+    stats = {key: np.atleast_1d(np.array(value)) for key, value in flatten_dict(stats).items()}
     return unflatten_dict(stats)