diff --git a/src/lerobot/policies/factory.py b/src/lerobot/policies/factory.py
index b3fdf0626..9f250d72b 100644
--- a/src/lerobot/policies/factory.py
+++ b/src/lerobot/policies/factory.py
@@ -16,6 +16,7 @@
 
 import logging
 
+import torch
 from torch import nn
 
 from lerobot.configs.policies import PreTrainedConfig
@@ -34,7 +35,7 @@ from lerobot.policies.sac.reward_model.configuration_classifier import RewardCla
 from lerobot.policies.smolvla.configuration_smolvla import SmolVLAConfig
 from lerobot.policies.tdmpc.configuration_tdmpc import TDMPCConfig
 from lerobot.policies.vqbet.configuration_vqbet import VQBeTConfig
-from lerobot.processor.pipeline import RobotProcessor
+from lerobot.processor.pipeline import EnvTransition, RobotProcessor, TransitionIndex
 
 
 def get_policy_class(name: str) -> PreTrainedPolicy:
@@ -113,13 +114,13 @@ def make_processor(
     Each policy type has its own processor with specific preprocessing steps.
 
     Args:
-        policy_type: The type of policy to create a processor for (e.g., "act", "diffusion", etc.)
+        policy_cfg: The config of the policy to create a processor for (e.g., "act", "diffusion", etc.)
         pretrained_path: Optional path to load a pretrained processor from. If provided, loads
             the processor from this path instead of creating a new one.
         **kwargs: Additional keyword arguments passed to the processor creation.
 
     Returns:
-        RobotProcessor: The configured processor instance.
+        Tuple of (input_processor, output_processor) for the policy.
 
     Raises:
         NotImplementedError: If the policy type doesn't have a processor implemented.
@@ -133,51 +134,67 @@ def make_processor(
     if policy_cfg.type == "tdmpc":
         from lerobot.policies.tdmpc.processor_tdmpc import make_tdmpc_processor
 
-        return make_tdmpc_processor(policy_cfg, **kwargs)
+        processors = make_tdmpc_processor(policy_cfg, **kwargs)
 
     elif policy_cfg.type == "diffusion":
         from lerobot.policies.diffusion.processor_diffusion import make_diffusion_processor
 
-        return make_diffusion_processor(policy_cfg, **kwargs)
+        processors = make_diffusion_processor(policy_cfg, **kwargs)
 
     elif policy_cfg.type == "act":
         from lerobot.policies.act.processor_act import make_act_processor
 
-        return make_act_processor(policy_cfg, **kwargs)
+        processors = make_act_processor(policy_cfg, **kwargs)
 
     elif policy_cfg.type == "vqbet":
         from lerobot.policies.vqbet.processor_vqbet import make_vqbet_processor
 
-        return make_vqbet_processor(policy_cfg, **kwargs)
+        processors = make_vqbet_processor(policy_cfg, **kwargs)
 
     elif policy_cfg.type == "pi0":
         from lerobot.policies.pi0.processor_pi0 import make_pi0_processor
 
-        return make_pi0_processor(policy_cfg, **kwargs)
+        processors = make_pi0_processor(policy_cfg, **kwargs)
 
     elif policy_cfg.type == "pi0fast":
         from lerobot.policies.pi0fast.processor_pi0fast import make_pi0fast_processor
 
-        return make_pi0fast_processor(policy_cfg, **kwargs)
+        processors = make_pi0fast_processor(policy_cfg, **kwargs)
 
     elif policy_cfg.type == "sac":
         from lerobot.policies.sac.processor_sac import make_sac_processor
 
-        return make_sac_processor(policy_cfg, **kwargs)
+        processors = make_sac_processor(policy_cfg, **kwargs)
 
     elif policy_cfg.type == "reward_classifier":
         from lerobot.policies.sac.reward_model.processor_classifier import make_classifier_processor
 
-        return make_classifier_processor(policy_cfg, **kwargs)
+        processors = make_classifier_processor(policy_cfg, **kwargs)
 
     elif policy_cfg.type == "smolvla":
         from lerobot.policies.smolvla.processor_smolvla import make_smolvla_processor
 
-        return make_smolvla_processor(policy_cfg, **kwargs)
+        processors = make_smolvla_processor(policy_cfg, **kwargs)
 
     else:
         raise NotImplementedError(f"Processor for policy type '{policy_cfg.type}' is not implemented.")
 
+    # Helper hook function to detect NaNs in observation
+    def nan_detection_hook(step_idx: int, transition: EnvTransition) -> None:
+        observation = transition[TransitionIndex.OBSERVATION]
+        if observation is not None:
+            for key, value in observation.items():
+                if isinstance(value, torch.Tensor) and torch.isnan(value).any():
+                    logging.warning(f"NaN detected in observation key '{key}' after step {step_idx}: {value}")
+
+    # Attach the hook to all returned processors
+    if isinstance(processors, RobotProcessor):
+        processors = (processors,)  # Wrap single processor in tuple for consistency
+    for processor in processors:
+        processor.register_after_step_hook(nan_detection_hook)
+
+    return processors
+
 
 def make_policy(
     cfg: PreTrainedConfig,