diff --git a/src/lerobot/policies/g05/configuration_g05.py b/src/lerobot/policies/g05/configuration_g05.py
index c693c4800..ea05fc5bd 100644
--- a/src/lerobot/policies/g05/configuration_g05.py
+++ b/src/lerobot/policies/g05/configuration_g05.py
@@ -246,7 +246,10 @@ class G05Config(PreTrainedConfig):
                 raise ValueError("normalization_clip must be an increasing (minimum, maximum) pair.")
         self.relative_exclude_joints = tuple(self.relative_exclude_joints)
         self.action_feature_names = tuple(self.action_feature_names)
-        if not self.camera_sizes and self.embodiment in G05_CAMERA_SIZE_PROFILES:
+        if self.embodiment in G05_CAMERA_SIZE_PROFILES and set(self.camera_sizes) != set(self.camera_order):
+            # Switching embodiment on a packaged checkpoint keeps the previous
+            # embodiment's camera entries (config-file/CLI dict values merge
+            # instead of replacing); rebuild sizes from the named profile.
             self.camera_sizes = G05_CAMERA_SIZE_PROFILES[self.embodiment].copy()
         if self.num_input_images == 0:
             self.num_input_images = len(self.camera_order) * self.n_obs_steps
diff --git a/tests/policies/g05/test_g05.py b/tests/policies/g05/test_g05.py
index 000678272..d1f0192ce 100644
--- a/tests/policies/g05/test_g05.py
+++ b/tests/policies/g05/test_g05.py
@@ -13,7 +13,7 @@ from torch import nn
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.configs.types import FeatureType, PolicyFeature
 from lerobot.policies.factory import get_policy_class, make_policy_config, make_pre_post_processors
-from lerobot.policies.g05.configuration_g05 import G05_EMBODIMENT_MAPPINGS, G05Config
+from lerobot.policies.g05.configuration_g05 import G05_CAMERA_PROFILES, G05_EMBODIMENT_MAPPINGS, G05Config
 from lerobot.policies.g05.modeling_g05 import G05Policy
 from lerobot.policies.g05.native_g05 import G05_RUNTIME_PREDICT_COT, G05NativeBackend
 from lerobot.processor import PolicyProcessorPipeline
@@ -830,3 +830,30 @@ def test_gated_checkpoint_loads_strictly():
     checkpoint = Path(os.environ["LEROBOT_G05_CHECKPOINT"])
     policy = G05Policy.from_pretrained(checkpoint, local_files_only=True, strict=True)
     assert policy.config.source_checkpoint_revision
+
+
+def test_project_stats_passes_dataset_count_through():
+    config = _config(normalization_mode="q01_q99")
+    stats = {
+        OBS_STATE: {"q01": torch.zeros(7), "q99": torch.ones(7), "count": torch.tensor([100])},
+        ACTION: {"q01": torch.zeros(7), "q99": torch.ones(7), "count": torch.tensor([100])},
+    }
+
+    make_pre_post_processors(config, dataset_stats=stats)
+
+
+def test_named_embodiment_rebuilds_stale_camera_sizes():
+    config = G05Config(
+        checkpoint_profile="custom",
+        embodiment="robotwin",
+        raw_state_dim=14,
+        raw_action_dim=14,
+        camera_order=G05_CAMERA_PROFILES["robotwin"],
+        camera_sizes={
+            **dict.fromkeys(G05_CAMERA_PROFILES["robotwin"], (256, 256)),
+            "observation.images.stale_camera": (256, 256),
+        },
+        device="cpu",
+    )
+
+    assert set(config.camera_sizes) == set(G05_CAMERA_PROFILES["robotwin"])