diff --git a/tests/test_network_shapes.py b/tests/test_network_shapes.py
index 32e9898..e1a7a1a 100644
--- a/tests/test_network_shapes.py
+++ b/tests/test_network_shapes.py
@@ -2,8 +2,8 @@ import jax
 import jax.numpy as jnp
 from brittle_star_project.environment.padded_obs_wrapper import (
     compute_padding_masks,
-    pad_observations_batched,
 )
+from brittle_star_project.environment.obs_processing import create_obs_processor
 
 # We use Actor and OneDenseLayerMLP (as the critic) based on your mlps.py
 from brittle_star_project.MLPs.mlps import Actor, OneDenseLayerMLP
@@ -20,21 +20,10 @@ def test_centralized_forward_pass_with_padding():
         "segment_contact": jnp.zeros((batch_size, 14)),
     }
 
-    # 2. Pad Observation using the boolean scattering wrapper
+    # 2. Process and Pad Observation
     masks = compute_padding_masks(segments_per_arm=(4, 0, 4, 2, 4))
-    padded_obs = pad_observations_batched(amputated_obs, masks)
-
-    # Assertions to ensure padding sizes are correct (40 joints, 20 segments)
-    assert padded_obs["joint_position"].shape == (batch_size, 40), "Padding failed for joint keys"
-    assert padded_obs["segment_contact"].shape == (batch_size, 20), (
-        "Padding failed for segment keys"
-    )
-
-    # 3. Concatenate for Centralized MLP (simulating the global state vector)
-    global_state = jnp.concatenate(
-        [padded_obs["joint_position"], padded_obs["joint_velocity"], padded_obs["segment_contact"]],
-        axis=-1,
-    )
+    obs_processor = create_obs_processor(bounds_dict={}, padding_masks=masks)
+    global_state = obs_processor(amputated_obs)
 
     # 40 + 40 + 20 = 100 dimensions
     assert global_state.shape == (batch_size, 100), (