diff --git a/src/lerobot/processor/__init__.py b/src/lerobot/processor/__init__.py
index 3f0267eae..c5c4af9fa 100644
--- a/src/lerobot/processor/__init__.py
+++ b/src/lerobot/processor/__init__.py
@@ -25,9 +25,11 @@ from .pipeline import (
     ActionProcessor,
     DoneProcessor,
     EnvTransition,
+    IdentityProcessor,
     InfoProcessor,
     ObservationProcessor,
     ProcessorStep,
+    ProcessorStepRegistry,
     RewardProcessor,
     RobotProcessor,
     TruncatedProcessor,
@@ -39,12 +41,14 @@ __all__ = [
     "DeviceProcessor",
     "DoneProcessor",
     "EnvTransition",
+    "IdentityProcessor",
     "ImageProcessor",
     "InfoProcessor",
     "NormalizerProcessor",
     "UnnormalizerProcessor",
     "ObservationProcessor",
     "ProcessorStep",
+    "ProcessorStepRegistry",
     "RenameProcessor",
     "RewardProcessor",
     "RobotProcessor",
diff --git a/src/lerobot/processor/device_processor.py b/src/lerobot/processor/device_processor.py
index 0ff6ef9da..7dcd8abda 100644
--- a/src/lerobot/processor/device_processor.py
+++ b/src/lerobot/processor/device_processor.py
@@ -33,6 +33,9 @@ class DeviceProcessor:
 
     device: str = "cpu"
 
+    def __post_init__(self):
+        self.non_blocking = "cuda" in self.device
+
     def __call__(self, transition: EnvTransition) -> EnvTransition:
         observation: dict[str, torch.Tensor] = transition[TransitionIndex.OBSERVATION]
         action = transition[TransitionIndex.ACTION]
@@ -43,7 +46,9 @@ class DeviceProcessor:
         complementary_data = transition[TransitionIndex.COMPLEMENTARY_DATA]
 
         if observation is not None:
-            observation = {k: v.to(self.device) for k, v in observation.items()}
+            observation = {
+                k: v.to(self.device, non_blocking=self.non_blocking) for k, v in observation.items()
+            }
         if action is not None:
             action = action.to(self.device)
 
diff --git a/src/lerobot/processor/normalize_processor.py b/src/lerobot/processor/normalize_processor.py
index 6b3cae5be..8e836dd50 100644
--- a/src/lerobot/processor/normalize_processor.py
+++ b/src/lerobot/processor/normalize_processor.py
@@ -1,12 +1,13 @@
 from __future__ import annotations
 
 from dataclasses import dataclass, field
-from typing import Any, Mapping
+from typing import Any, Mapping, Optional, Set
 
 import numpy as np
 import torch
 from torch import Tensor
 
+from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.processor.pipeline import EnvTransition, ProcessorStepRegistry, TransitionIndex
 
@@ -45,8 +46,18 @@ class NormalizerProcessor:
     the normalize_keys parameter.
     """
 
-    stats: dict[str, dict[str, Any]]
-    normalize_keys: set[str] | None = None
+    # Features and normalisation map are mandatory to match the design of normalize.py
+    features: dict[str, PolicyFeature]
+    norm_map: dict[FeatureType, NormalizationMode]
+
+    # Pre-computed statistics coming from dataset.meta.stats for instance.
+    stats: Optional[dict[str, dict[str, Any]]] = None
+
+    # Explicit subset of keys to normalise. If ``None`` every key (except
+    # "action") found in ``stats`` will be normalised. Using a ``set`` makes
+    # membership checks O(1).
+    normalize_keys: Optional[Set[str]] = None
+
     eps: float = 1e-8
 
     _tensor_stats: dict[str, dict[str, Tensor]] = field(default_factory=dict, init=False, repr=False)
@@ -55,24 +66,48 @@ class NormalizerProcessor:
     def from_lerobot_dataset(
         cls,
         dataset: LeRobotDataset,
+        features: dict[str, PolicyFeature],
+        norm_map: dict[FeatureType, NormalizationMode],
         *,
-        normalize_keys: set[str] | None = None,
+        normalize_keys: Optional[Set[str]] = None,
         eps: float = 1e-8,
-    ) -> NormalizerProcessor:
-        return cls(stats=dataset.meta.stats, normalize_keys=normalize_keys, eps=eps)
+    ) -> "NormalizerProcessor":
+        """Factory helper that pulls statistics from a :class:`LeRobotDataset`.
+
+        The features and norm_map parameters are mandatory to match the design
+        pattern used in normalize.py.
+        """
+
+        return cls(
+            features=features,
+            norm_map=norm_map,
+            stats=dataset.meta.stats,
+            normalize_keys=normalize_keys,
+            eps=eps,
+        )
 
     def __post_init__(self):
+        # Convert statistics once so we avoid repeated numpy→Tensor conversions
+        # during runtime.
+        self.stats = self.stats or {}
         self._tensor_stats = _convert_stats_to_tensors(self.stats)
 
+        # Ensure *normalize_keys* is a set for fast look-ups and compare by
+        # value later when returning the configuration.
+        if self.normalize_keys is not None and not isinstance(self.normalize_keys, set):
+            self.normalize_keys = set(self.normalize_keys)
+
     def _normalize_obs(self, observation):
         if observation is None:
             return None
 
-        keys_to_norm = (
-            self.normalize_keys
-            if self.normalize_keys is not None
-            else {k for k in self._tensor_stats if k != "action"}
-        )
+        # Decide which keys should be normalised for this call.
+        if self.normalize_keys is not None:
+            keys_to_norm = self.normalize_keys
+        else:
+            # Use feature map to skip action keys.
+            keys_to_norm = {k for k, ft in self.features.items() if ft.type is not FeatureType.ACTION}
+
         processed = dict(observation)
         for key in keys_to_norm:
             if key not in processed or key not in self._tensor_stats:
@@ -126,7 +161,11 @@ class NormalizerProcessor:
         )
 
     def get_config(self) -> dict[str, Any]:
-        return {"normalize_keys": list(self.normalize_keys) if self.normalize_keys else None, "eps": self.eps}
+        config = {"eps": self.eps}
+        if self.normalize_keys is not None:
+            # Serialise as a list for YAML / JSON friendliness
+            config["normalize_keys"] = sorted(self.normalize_keys)
+        return config
 
     def state_dict(self) -> dict[str, Tensor]:
         flat = {}
@@ -154,8 +193,9 @@ class UnnormalizerProcessor:
     transform.
     """
 
-    stats: dict[str, dict[str, Any]]
-    unnormalize_keys: set[str] | None = None
+    features: dict[str, PolicyFeature]
+    norm_map: dict[FeatureType, NormalizationMode]
+    stats: Optional[dict[str, dict[str, Any]]] = None
     eps: float = 1e-8
 
     _tensor_stats: dict[str, dict[str, Tensor]] = field(default_factory=dict, init=False, repr=False)
@@ -164,23 +204,21 @@ class UnnormalizerProcessor:
     def from_lerobot_dataset(
         cls,
         dataset: LeRobotDataset,
+        features: dict[str, PolicyFeature],
+        norm_map: dict[FeatureType, NormalizationMode],
         *,
-        unnormalize_keys: set[str] | None = None,
         eps: float = 1e-8,
-    ) -> UnnormalizerProcessor:
-        return cls(stats=dataset.meta.stats, unnormalize_keys=unnormalize_keys, eps=eps)
+    ) -> "UnnormalizerProcessor":
+        return cls(features=features, norm_map=norm_map, stats=dataset.meta.stats, eps=eps)
 
     def __post_init__(self):
+        self.stats = self.stats or {}
         self._tensor_stats = _convert_stats_to_tensors(self.stats)
 
     def _unnormalize_obs(self, observation):
         if observation is None:
             return None
-        keys = (
-            self.unnormalize_keys
-            if self.unnormalize_keys is not None
-            else {k for k in self._tensor_stats if k != "action"}
-        )
+        keys = [k for k, ft in self.features.items() if ft.type is not FeatureType.ACTION]
         processed = dict(observation)
         for key in keys:
             if key not in processed or key not in self._tensor_stats:
@@ -231,10 +269,7 @@ class UnnormalizerProcessor:
         )
 
     def get_config(self) -> dict[str, Any]:
-        return {
-            "unnormalize_keys": list(self.unnormalize_keys) if self.unnormalize_keys else None,
-            "eps": self.eps,
-        }
+        return {"eps": self.eps}
 
     def state_dict(self) -> dict[str, Tensor]:
         flat = {}
diff --git a/src/lerobot/processor/pipeline.py b/src/lerobot/processor/pipeline.py
index ac900fcff..7c88f6d3a 100644
--- a/src/lerobot/processor/pipeline.py
+++ b/src/lerobot/processor/pipeline.py
@@ -42,7 +42,7 @@ class TransitionIndex(IntEnum):
 
 # (observation, action, reward, done, truncated, info, complementary_data)
 EnvTransition = Tuple[
-    Any | None,  # observation
+    dict[str, Any] | None,  # observation
     Any | None,  # action
     float | None,  # reward
     bool | None,  # done
@@ -162,6 +162,79 @@ class ProcessorStep(Protocol):
     def reset(self) -> None: ...
 
 
+def _default_batch_to_transition(batch: dict[str, Any]) -> EnvTransition:  # noqa: D401
+    """Convert a *batch* dict coming from Learobot replay/dataset code into an
+    ``EnvTransition`` tuple.
+
+    The function is intentionally **strictly positional** – it maps well known
+    keys to the fixed slot order used inside the pipeline.  Missing keys are
+    filled with sane defaults (``None`` or ``0.0``/``False``).
+
+    Keys recognised (case-sensitive):
+
+    * "observation.*" (keys starting with "observation." are grouped into observation dict)
+    * "action"
+    * "next.reward"
+    * "next.done"
+    * "next.truncated"
+    * "info"
+
+    Additional keys are ignored so that existing dataloaders can carry extra
+    metadata without breaking the processor.
+    """
+
+    # Handle observation and observation.* keys
+    observation_keys = {k: v for k, v in batch.items() if k.startswith("observation.")}
+
+    observation = None
+    if observation_keys:
+        observation = {}
+        # Add observation.* keys to the observation dict, removing the "observation." prefix
+        for key, value in observation_keys.items():
+            observation[key] = value
+
+    return (
+        observation,
+        batch.get("action"),
+        batch.get("next.reward", 0.0),
+        batch.get("next.done", False),
+        batch.get("next.truncated", False),
+        batch.get("info", {}),
+        {},
+    )
+
+
+def _default_transition_to_batch(transition: EnvTransition) -> dict[str, Any]:  # noqa: D401
+    """Inverse of :pyfunc:`_default_batch_to_transition`. Returns a dict with
+    the canonical field names used throughout *LeRobot*.
+    """
+
+    (
+        observation,
+        action,
+        reward,
+        done,
+        truncated,
+        info,
+        _,
+    ) = transition
+
+    batch = {
+        "action": action,
+        "next.reward": reward,
+        "next.done": done,
+        "next.truncated": truncated,
+        "info": info,
+    }
+
+    # Handle observation - flatten dict to observation.* keys if it's a dict
+    if isinstance(observation, dict):
+        # Check if this looks like a dict that was created from observation.* keys
+        for key, value in observation.items():
+            batch[key] = value
+    return batch
+
+
 @dataclass
 class RobotProcessor(ModelHubMixin):
     """
@@ -200,6 +273,13 @@ class RobotProcessor(ModelHubMixin):
     name: str = "RobotProcessor"
     seed: int | None = None
 
+    to_transition: Callable[[dict[str, Any]], EnvTransition] = field(
+        default_factory=lambda: _default_batch_to_transition, repr=False
+    )
+    to_batch: Callable[[EnvTransition], dict[str, Any]] = field(
+        default_factory=lambda: _default_transition_to_batch, repr=False
+    )
+
     # Processor-level hooks
     # A hook can optionally return a modified transition.  If it returns
     # ``None`` the current value is left untouched.
@@ -211,14 +291,29 @@ class RobotProcessor(ModelHubMixin):
     )
     reset_hooks: list[Callable[[], None]] = field(default_factory=list, repr=False)
 
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        """Run *transition* through every step, firing hooks on the way."""
+    def __call__(self, data: EnvTransition | dict[str, Any]):
+        """Process *data* through all steps.
 
-        # Basic validation with helpful error message
+        The method accepts **either** the classic :pydata:`EnvTransition` tuple
+        **or** a *batch* dictionary (like the ones returned by
+        :class:`lerobot.utils.buffer.ReplayBuffer` or
+        :class:`lerobot.datasets.lerobot_dataset.LeRobotDataset`).  If a dict is
+        supplied it is first converted to the internal tuple format using
+        :pyattr:`to_transition`; after all steps are executed the tuple is
+        transformed back into a dict with :pyattr:`to_batch` and the result is
+        returned – thereby preserving the caller's original data type.
+        """
+
+        called_with_batch = isinstance(data, dict)
+
+        transition = self.to_transition(data) if called_with_batch else data
+
+        # Basic validation with helpful error message for tuple input
         if not isinstance(transition, tuple) or len(transition) != 7:
             raise ValueError(
-                f"EnvTransition must be a 7-tuple of (observation, action, reward, done, truncated, info, complementary_data), "
-                f"got {type(transition).__name__} with length {len(transition) if hasattr(transition, '__len__') else 'unknown'}"
+                "EnvTransition must be a 7-tuple of (observation, action, reward, done, "
+                "truncated, info, complementary_data). "
+                f"Got {type(transition).__name__} with length {len(transition) if hasattr(transition, '__len__') else 'unknown'}."
             )
 
         for idx, processor_step in enumerate(self.steps):
@@ -234,7 +329,7 @@ class RobotProcessor(ModelHubMixin):
                 if updated is not None:
                     transition = updated
 
-        return transition
+        return self.to_batch(transition) if called_with_batch else transition
 
     def step_through(self, transition: EnvTransition) -> Iterable[EnvTransition]:
         """Yield the intermediate Transition instances after each processor step."""
@@ -737,3 +832,22 @@ class InfoProcessor:
             *transition[TransitionIndex.COMPLEMENTARY_DATA :],
         )
         return transition
+
+
+class IdentityProcessor:
+    """Identity processor that does nothing."""
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        return transition
+
+    def get_config(self) -> dict[str, Any]:
+        return {}
+
+    def state_dict(self) -> dict[str, torch.Tensor]:
+        return {}
+
+    def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
+        pass
+
+    def reset(self) -> None:
+        pass
diff --git a/tests/processor/test_batch_conversion.py b/tests/processor/test_batch_conversion.py
new file mode 100644
index 000000000..5b399a675
--- /dev/null
+++ b/tests/processor/test_batch_conversion.py
@@ -0,0 +1,288 @@
+import torch
+
+from lerobot.processor.pipeline import (
+    RobotProcessor,
+    TransitionIndex,
+    _default_batch_to_transition,
+    _default_transition_to_batch,
+)
+
+
+def _dummy_batch():
+    """Create a dummy batch using the new format with observation.* and next.* keys."""
+    return {
+        "observation.image.left": torch.randn(1, 3, 128, 128),
+        "observation.image.right": torch.randn(1, 3, 128, 128),
+        "observation.state": torch.tensor([[0.1, 0.2, 0.3, 0.4]]),
+        "action": torch.tensor([[0.5]]),
+        "next.reward": 1.0,
+        "next.done": False,
+        "next.truncated": False,
+        "info": {"key": "value"},
+    }
+
+
+def test_observation_grouping_roundtrip():
+    """Test that observation.* keys are properly grouped and ungrouped."""
+    proc = RobotProcessor([])
+    batch_in = _dummy_batch()
+    batch_out = proc(batch_in)
+
+    # Check that all observation.* keys are preserved
+    original_obs_keys = {k: v for k, v in batch_in.items() if k.startswith("observation.")}
+    reconstructed_obs_keys = {k: v for k, v in batch_out.items() if k.startswith("observation.")}
+
+    assert set(original_obs_keys.keys()) == set(reconstructed_obs_keys.keys())
+
+    # Check tensor values
+    assert torch.allclose(batch_out["observation.image.left"], batch_in["observation.image.left"])
+    assert torch.allclose(batch_out["observation.image.right"], batch_in["observation.image.right"])
+    assert torch.allclose(batch_out["observation.state"], batch_in["observation.state"])
+
+    # Check other fields
+    assert torch.allclose(batch_out["action"], batch_in["action"])
+    assert batch_out["next.reward"] == batch_in["next.reward"]
+    assert batch_out["next.done"] == batch_in["next.done"]
+    assert batch_out["next.truncated"] == batch_in["next.truncated"]
+    assert batch_out["info"] == batch_in["info"]
+
+
+def test_batch_to_transition_observation_grouping():
+    """Test that _default_batch_to_transition correctly groups observation.* keys."""
+    batch = {
+        "observation.image.top": torch.randn(1, 3, 128, 128),
+        "observation.image.left": torch.randn(1, 3, 128, 128),
+        "observation.state": [1, 2, 3, 4],
+        "action": "action_data",
+        "next.reward": 1.5,
+        "next.done": True,
+        "next.truncated": False,
+        "info": {"episode": 42},
+    }
+
+    transition = _default_batch_to_transition(batch)
+
+    # Check observation is a dict with all observation.* keys
+    assert isinstance(transition[TransitionIndex.OBSERVATION], dict)
+    assert "observation.image.top" in transition[TransitionIndex.OBSERVATION]
+    assert "observation.image.left" in transition[TransitionIndex.OBSERVATION]
+    assert "observation.state" in transition[TransitionIndex.OBSERVATION]
+
+    # Check values are preserved
+    assert torch.allclose(
+        transition[TransitionIndex.OBSERVATION]["observation.image.top"], batch["observation.image.top"]
+    )
+    assert torch.allclose(
+        transition[TransitionIndex.OBSERVATION]["observation.image.left"], batch["observation.image.left"]
+    )
+    assert transition[TransitionIndex.OBSERVATION]["observation.state"] == [1, 2, 3, 4]
+
+    # Check other fields
+    assert transition[TransitionIndex.ACTION] == "action_data"
+    assert transition[TransitionIndex.REWARD] == 1.5
+    assert transition[TransitionIndex.DONE]
+    assert not transition[TransitionIndex.TRUNCATED]
+    assert transition[TransitionIndex.INFO] == {"episode": 42}
+    assert transition[TransitionIndex.COMPLEMENTARY_DATA] == {}
+
+
+def test_transition_to_batch_observation_flattening():
+    """Test that _default_transition_to_batch correctly flattens observation dict."""
+    observation_dict = {
+        "observation.image.top": torch.randn(1, 3, 128, 128),
+        "observation.image.left": torch.randn(1, 3, 128, 128),
+        "observation.state": [1, 2, 3, 4],
+    }
+
+    transition = (
+        observation_dict,  # observation
+        "action_data",  # action
+        1.5,  # reward
+        True,  # done
+        False,  # truncated
+        {"episode": 42},  # info
+        {},  # complementary_data
+    )
+
+    batch = _default_transition_to_batch(transition)
+
+    # Check that observation.* keys are flattened back to batch
+    assert "observation.image.top" in batch
+    assert "observation.image.left" in batch
+    assert "observation.state" in batch
+
+    # Check values are preserved
+    assert torch.allclose(batch["observation.image.top"], observation_dict["observation.image.top"])
+    assert torch.allclose(batch["observation.image.left"], observation_dict["observation.image.left"])
+    assert batch["observation.state"] == [1, 2, 3, 4]
+
+    # Check other fields are mapped to next.* format
+    assert batch["action"] == "action_data"
+    assert batch["next.reward"] == 1.5
+    assert batch["next.done"]
+    assert not batch["next.truncated"]
+    assert batch["info"] == {"episode": 42}
+
+
+def test_no_observation_keys():
+    """Test behavior when there are no observation.* keys."""
+    batch = {
+        "action": "action_data",
+        "next.reward": 2.0,
+        "next.done": False,
+        "next.truncated": True,
+        "info": {"test": "no_obs"},
+    }
+
+    transition = _default_batch_to_transition(batch)
+
+    # Observation should be None when no observation.* keys
+    assert transition[TransitionIndex.OBSERVATION] is None
+
+    # Check other fields
+    assert transition[TransitionIndex.ACTION] == "action_data"
+    assert transition[TransitionIndex.REWARD] == 2.0
+    assert not transition[TransitionIndex.DONE]
+    assert transition[TransitionIndex.TRUNCATED]
+    assert transition[TransitionIndex.INFO] == {"test": "no_obs"}
+
+    # Round trip should work
+    reconstructed_batch = _default_transition_to_batch(transition)
+    assert reconstructed_batch["action"] == "action_data"
+    assert reconstructed_batch["next.reward"] == 2.0
+    assert not reconstructed_batch["next.done"]
+    assert reconstructed_batch["next.truncated"]
+    assert reconstructed_batch["info"] == {"test": "no_obs"}
+
+
+def test_minimal_batch():
+    """Test with minimal batch containing only observation.* and action."""
+    batch = {"observation.state": "minimal_state", "action": "minimal_action"}
+
+    transition = _default_batch_to_transition(batch)
+
+    # Check observation
+    assert transition[TransitionIndex.OBSERVATION] == {"observation.state": "minimal_state"}
+    assert transition[TransitionIndex.ACTION] == "minimal_action"
+
+    # Check defaults
+    assert transition[TransitionIndex.REWARD] == 0.0
+    assert not transition[TransitionIndex.DONE]
+    assert not transition[TransitionIndex.TRUNCATED]
+    assert transition[TransitionIndex.INFO] == {}
+    assert transition[TransitionIndex.COMPLEMENTARY_DATA] == {}
+
+    # Round trip
+    reconstructed_batch = _default_transition_to_batch(transition)
+    assert reconstructed_batch["observation.state"] == "minimal_state"
+    assert reconstructed_batch["action"] == "minimal_action"
+    assert reconstructed_batch["next.reward"] == 0.0
+    assert not reconstructed_batch["next.done"]
+    assert not reconstructed_batch["next.truncated"]
+    assert reconstructed_batch["info"] == {}
+
+
+def test_empty_batch():
+    """Test behavior with empty batch."""
+    batch = {}
+
+    transition = _default_batch_to_transition(batch)
+
+    # All fields should have defaults
+    assert transition[TransitionIndex.OBSERVATION] is None
+    assert transition[TransitionIndex.ACTION] is None
+    assert transition[TransitionIndex.REWARD] == 0.0
+    assert not transition[TransitionIndex.DONE]
+    assert not transition[TransitionIndex.TRUNCATED]
+    assert transition[TransitionIndex.INFO] == {}
+    assert transition[TransitionIndex.COMPLEMENTARY_DATA] == {}
+
+    # Round trip
+    reconstructed_batch = _default_transition_to_batch(transition)
+    assert reconstructed_batch["action"] is None
+    assert reconstructed_batch["next.reward"] == 0.0
+    assert not reconstructed_batch["next.done"]
+    assert not reconstructed_batch["next.truncated"]
+    assert reconstructed_batch["info"] == {}
+
+
+def test_complex_nested_observation():
+    """Test with complex nested observation data."""
+    batch = {
+        "observation.image.top": {"image": torch.randn(1, 3, 128, 128), "timestamp": 1234567890},
+        "observation.image.left": {"image": torch.randn(1, 3, 128, 128), "timestamp": 1234567891},
+        "observation.state": torch.randn(7),
+        "action": torch.randn(8),
+        "next.reward": 3.14,
+        "next.done": False,
+        "next.truncated": True,
+        "info": {"episode_length": 200, "success": True},
+    }
+
+    transition = _default_batch_to_transition(batch)
+    reconstructed_batch = _default_transition_to_batch(transition)
+
+    # Check that all observation keys are preserved
+    original_obs_keys = {k for k in batch.keys() if k.startswith("observation.")}
+    reconstructed_obs_keys = {k for k in reconstructed_batch.keys() if k.startswith("observation.")}
+
+    assert original_obs_keys == reconstructed_obs_keys
+
+    # Check tensor values
+    assert torch.allclose(batch["observation.state"], reconstructed_batch["observation.state"])
+
+    # Check nested dict with tensors
+    assert torch.allclose(
+        batch["observation.image.top"]["image"], reconstructed_batch["observation.image.top"]["image"]
+    )
+    assert torch.allclose(
+        batch["observation.image.left"]["image"], reconstructed_batch["observation.image.left"]["image"]
+    )
+
+    # Check action tensor
+    assert torch.allclose(batch["action"], reconstructed_batch["action"])
+
+    # Check other fields
+    assert batch["next.reward"] == reconstructed_batch["next.reward"]
+    assert batch["next.done"] == reconstructed_batch["next.done"]
+    assert batch["next.truncated"] == reconstructed_batch["next.truncated"]
+    assert batch["info"] == reconstructed_batch["info"]
+
+
+def test_custom_converter():
+    """Test that custom converters can still be used."""
+
+    def to_tr(batch):
+        # Custom converter that modifies the reward
+        tr = _default_batch_to_transition(batch)
+        # Double the reward
+        reward = tr[TransitionIndex.REWARD] * 2 if tr[TransitionIndex.REWARD] is not None else 0.0
+        return (
+            tr[TransitionIndex.OBSERVATION],
+            tr[TransitionIndex.ACTION],
+            reward,
+            tr[TransitionIndex.DONE],
+            tr[TransitionIndex.TRUNCATED],
+            tr[TransitionIndex.INFO],
+            tr[TransitionIndex.COMPLEMENTARY_DATA],
+        )
+
+    def to_batch(tr):
+        # Custom converter that adds a custom field
+        batch = _default_transition_to_batch(tr)
+        batch["custom_field"] = "custom_value"
+        return batch
+
+    proc = RobotProcessor([], to_transition=to_tr, to_batch=to_batch)
+    batch = _dummy_batch()
+    out = proc(batch)
+
+    # Check that custom modifications were applied
+    assert out["next.reward"] == batch["next.reward"] * 2
+    assert out["custom_field"] == "custom_value"
+
+    # Check that observation.* keys are still preserved
+    original_obs_keys = {k: v for k, v in batch.items() if k.startswith("observation.")}
+    output_obs_keys = {k: v for k, v in out.items() if k.startswith("observation.")}
+
+    assert set(original_obs_keys.keys()) == set(output_obs_keys.keys())
diff --git a/tests/processor/test_normalize_processor.py b/tests/processor/test_normalize_processor.py
index e476ec27f..0c48433e8 100644
--- a/tests/processor/test_normalize_processor.py
+++ b/tests/processor/test_normalize_processor.py
@@ -4,6 +4,7 @@ import numpy as np
 import pytest
 import torch
 
+from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
 from lerobot.processor.normalize_processor import (
     NormalizerProcessor,
     UnnormalizerProcessor,
@@ -76,6 +77,21 @@ def test_unsupported_type():
         _convert_stats_to_tensors(stats)
 
 
+# Helper functions to create feature maps and norm maps
+def _create_observation_features():
+    return {
+        "observation.image": PolicyFeature(FeatureType.VISUAL, (3, 96, 96)),
+        "observation.state": PolicyFeature(FeatureType.STATE, (2,)),
+    }
+
+
+def _create_observation_norm_map():
+    return {
+        FeatureType.VISUAL: NormalizationMode.MEAN_STD,
+        FeatureType.STATE: NormalizationMode.MIN_MAX,
+    }
+
+
 # Fixtures for observation normalisation tests using NormalizerProcessor
 @pytest.fixture
 def observation_stats():
@@ -94,7 +110,9 @@ def observation_stats():
 @pytest.fixture
 def observation_normalizer(observation_stats):
     """Return a NormalizerProcessor that only has observation stats (no action)."""
-    return NormalizerProcessor(stats=observation_stats)
+    features = _create_observation_features()
+    norm_map = _create_observation_norm_map()
+    return NormalizerProcessor(features=features, norm_map=norm_map, stats=observation_stats)
 
 
 def test_mean_std_normalization(observation_normalizer):
@@ -129,7 +147,11 @@ def test_min_max_normalization(observation_normalizer):
 
 
 def test_selective_normalization(observation_stats):
-    normalizer = NormalizerProcessor(stats=observation_stats, normalize_keys={"observation.image"})
+    features = _create_observation_features()
+    norm_map = _create_observation_norm_map()
+    normalizer = NormalizerProcessor(
+        features=features, norm_map=norm_map, stats=observation_stats, normalize_keys={"observation.image"}
+    )
 
     observation = {
         "observation.image": torch.tensor([0.7, 0.5, 0.3]),
@@ -148,7 +170,9 @@ def test_selective_normalization(observation_stats):
 
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
 def test_device_compatibility(observation_stats):
-    normalizer = NormalizerProcessor(stats=observation_stats)
+    features = _create_observation_features()
+    norm_map = _create_observation_norm_map()
+    normalizer = NormalizerProcessor(features=features, norm_map=norm_map, stats=observation_stats)
     observation = {
         "observation.image": torch.tensor([0.7, 0.5, 0.3]).cuda(),
     }
@@ -165,10 +189,19 @@ def test_from_lerobot_dataset():
     mock_dataset = Mock()
     mock_dataset.meta.stats = {
         "observation.image": {"mean": [0.5], "std": [0.2]},
-        "action": {"mean": [0.0], "std": [1.0]},  # Should be filtered out
+        "action": {"mean": [0.0], "std": [1.0]},
     }
 
-    normalizer = NormalizerProcessor.from_lerobot_dataset(mock_dataset)
+    features = {
+        "observation.image": PolicyFeature(FeatureType.VISUAL, (3, 96, 96)),
+        "action": PolicyFeature(FeatureType.ACTION, (1,)),
+    }
+    norm_map = {
+        FeatureType.VISUAL: NormalizationMode.MEAN_STD,
+        FeatureType.ACTION: NormalizationMode.MEAN_STD,
+    }
+
+    normalizer = NormalizerProcessor.from_lerobot_dataset(mock_dataset, features, norm_map)
 
     # Both observation and action statistics should be present in tensor stats
     assert "observation.image" in normalizer._tensor_stats
@@ -180,7 +213,9 @@ def test_state_dict_save_load(observation_normalizer):
     state_dict = observation_normalizer.state_dict()
 
     # Create new normalizer and load state
-    new_normalizer = NormalizerProcessor(stats={})
+    features = _create_observation_features()
+    norm_map = _create_observation_norm_map()
+    new_normalizer = NormalizerProcessor(features=features, norm_map=norm_map, stats={})
     new_normalizer.load_state_dict(state_dict)
 
     # Test that it works the same
@@ -210,8 +245,30 @@ def action_stats_min_max():
     }
 
 
+def _create_action_features():
+    return {
+        "action": PolicyFeature(FeatureType.ACTION, (3,)),
+    }
+
+
+def _create_action_norm_map_mean_std():
+    return {
+        FeatureType.ACTION: NormalizationMode.MEAN_STD,
+    }
+
+
+def _create_action_norm_map_min_max():
+    return {
+        FeatureType.ACTION: NormalizationMode.MIN_MAX,
+    }
+
+
 def test_mean_std_unnormalization(action_stats_mean_std):
-    unnormalizer = UnnormalizerProcessor(stats={"action": action_stats_mean_std})
+    features = _create_action_features()
+    norm_map = _create_action_norm_map_mean_std()
+    unnormalizer = UnnormalizerProcessor(
+        features=features, norm_map=norm_map, stats={"action": action_stats_mean_std}
+    )
 
     normalized_action = torch.tensor([1.0, -0.5, 2.0])
     transition = (None, normalized_action, None, None, None, None, None)
@@ -225,7 +282,11 @@ def test_mean_std_unnormalization(action_stats_mean_std):
 
 
 def test_min_max_unnormalization(action_stats_min_max):
-    unnormalizer = UnnormalizerProcessor(stats={"action": action_stats_min_max})
+    features = _create_action_features()
+    norm_map = _create_action_norm_map_min_max()
+    unnormalizer = UnnormalizerProcessor(
+        features=features, norm_map=norm_map, stats={"action": action_stats_min_max}
+    )
 
     # Actions in [-1, 1]
     normalized_action = torch.tensor([0.0, -1.0, 1.0])
@@ -247,7 +308,11 @@ def test_min_max_unnormalization(action_stats_min_max):
 
 
 def test_numpy_action_input(action_stats_mean_std):
-    unnormalizer = UnnormalizerProcessor(stats={"action": action_stats_mean_std})
+    features = _create_action_features()
+    norm_map = _create_action_norm_map_mean_std()
+    unnormalizer = UnnormalizerProcessor(
+        features=features, norm_map=norm_map, stats={"action": action_stats_mean_std}
+    )
 
     normalized_action = np.array([1.0, -0.5, 2.0], dtype=np.float32)
     transition = (None, normalized_action, None, None, None, None, None)
@@ -261,7 +326,11 @@ def test_numpy_action_input(action_stats_mean_std):
 
 
 def test_none_action(action_stats_mean_std):
-    unnormalizer = UnnormalizerProcessor(stats={"action": action_stats_mean_std})
+    features = _create_action_features()
+    norm_map = _create_action_norm_map_mean_std()
+    unnormalizer = UnnormalizerProcessor(
+        features=features, norm_map=norm_map, stats={"action": action_stats_mean_std}
+    )
 
     transition = (None, None, None, None, None, None, None)
     result = unnormalizer(transition)
@@ -273,7 +342,9 @@ def test_none_action(action_stats_mean_std):
 def test_action_from_lerobot_dataset():
     mock_dataset = Mock()
     mock_dataset.meta.stats = {"action": {"mean": [0.0], "std": [1.0]}}
-    unnormalizer = UnnormalizerProcessor.from_lerobot_dataset(mock_dataset)
+    features = {"action": PolicyFeature(FeatureType.ACTION, (1,))}
+    norm_map = {FeatureType.ACTION: NormalizationMode.MEAN_STD}
+    unnormalizer = UnnormalizerProcessor.from_lerobot_dataset(mock_dataset, features, norm_map)
     assert "mean" in unnormalizer._tensor_stats["action"]
 
 
@@ -296,9 +367,27 @@ def full_stats():
     }
 
 
+def _create_full_features():
+    return {
+        "observation.image": PolicyFeature(FeatureType.VISUAL, (3, 96, 96)),
+        "observation.state": PolicyFeature(FeatureType.STATE, (2,)),
+        "action": PolicyFeature(FeatureType.ACTION, (2,)),
+    }
+
+
+def _create_full_norm_map():
+    return {
+        FeatureType.VISUAL: NormalizationMode.MEAN_STD,
+        FeatureType.STATE: NormalizationMode.MIN_MAX,
+        FeatureType.ACTION: NormalizationMode.MEAN_STD,
+    }
+
+
 @pytest.fixture
 def normalizer_processor(full_stats):
-    return NormalizerProcessor(stats=full_stats)
+    features = _create_full_features()
+    norm_map = _create_full_norm_map()
+    return NormalizerProcessor(features=features, norm_map=norm_map, stats=full_stats)
 
 
 def test_combined_normalization(normalizer_processor):
@@ -331,7 +420,12 @@ def test_processor_from_lerobot_dataset(full_stats):
     mock_dataset = Mock()
     mock_dataset.meta.stats = full_stats
 
-    processor = NormalizerProcessor.from_lerobot_dataset(mock_dataset, normalize_keys={"observation.image"})
+    features = _create_full_features()
+    norm_map = _create_full_norm_map()
+
+    processor = NormalizerProcessor.from_lerobot_dataset(
+        mock_dataset, features, norm_map, normalize_keys={"observation.image"}
+    )
 
     assert processor.normalize_keys == {"observation.image"}
     assert "observation.image" in processor._tensor_stats
@@ -339,7 +433,11 @@ def test_processor_from_lerobot_dataset(full_stats):
 
 
 def test_get_config(full_stats):
-    processor = NormalizerProcessor(stats=full_stats, normalize_keys={"observation.image"}, eps=1e-6)
+    features = _create_full_features()
+    norm_map = _create_full_norm_map()
+    processor = NormalizerProcessor(
+        features=features, norm_map=norm_map, stats=full_stats, normalize_keys={"observation.image"}, eps=1e-6
+    )
 
     config = processor.get_config()
     assert config == {"normalize_keys": ["observation.image"], "eps": 1e-6}
@@ -366,7 +464,9 @@ def test_integration_with_robot_processor(normalizer_processor):
 # Edge case tests
 def test_empty_observation():
     stats = {"observation.image": {"mean": [0.5], "std": [0.2]}}
-    normalizer = NormalizerProcessor(stats=stats)
+    features = {"observation.image": PolicyFeature(FeatureType.VISUAL, (3, 96, 96))}
+    norm_map = {FeatureType.VISUAL: NormalizationMode.MEAN_STD}
+    normalizer = NormalizerProcessor(features=features, norm_map=norm_map, stats=stats)
 
     transition = (None, None, None, None, None, None, None)
     result = normalizer(transition)
@@ -375,19 +475,23 @@ def test_empty_observation():
 
 
 def test_empty_stats():
-    normalizer = NormalizerProcessor(stats={})
+    features = {"observation.image": PolicyFeature(FeatureType.VISUAL, (3, 96, 96))}
+    norm_map = {FeatureType.VISUAL: NormalizationMode.MEAN_STD}
+    normalizer = NormalizerProcessor(features=features, norm_map=norm_map, stats={})
     observation = {"observation.image": torch.tensor([0.5])}
     transition = (observation, None, None, None, None, None, None)
 
     result = normalizer(transition)
-    # Should return observation unchanged
+    # Should return observation unchanged since no stats are available
     assert torch.allclose(result[0]["observation.image"], observation["observation.image"])
 
 
 def test_partial_stats():
     """If statistics are incomplete, the value should pass through unchanged."""
     stats = {"observation.image": {"mean": [0.5]}}  # Missing std / (min,max)
-    normalizer = NormalizerProcessor(stats=stats)
+    features = {"observation.image": PolicyFeature(FeatureType.VISUAL, (3, 96, 96))}
+    norm_map = {FeatureType.VISUAL: NormalizationMode.MEAN_STD}
+    normalizer = NormalizerProcessor(features=features, norm_map=norm_map, stats=stats)
     observation = {"observation.image": torch.tensor([0.7])}
     transition = (observation, None, None, None, None, None, None)
 
@@ -399,6 +503,9 @@ def test_missing_action_stats_no_error():
     mock_dataset = Mock()
     mock_dataset.meta.stats = {"observation.image": {"mean": [0.5], "std": [0.2]}}
 
-    processor = UnnormalizerProcessor.from_lerobot_dataset(mock_dataset)
+    features = {"observation.image": PolicyFeature(FeatureType.VISUAL, (3, 96, 96))}
+    norm_map = {FeatureType.VISUAL: NormalizationMode.MEAN_STD}
+
+    processor = UnnormalizerProcessor.from_lerobot_dataset(mock_dataset, features, norm_map)
     # The tensor stats should not contain the 'action' key
     assert "action" not in processor._tensor_stats