fix normalization for dtype

README.md

@@ -101,9 +101,6 @@
 ## Installation
 
 LeRobot works with Python 3.10+ and PyTorch 2.2+.
-
-### Environment Setup
-
 Create a virtual environment with Python 3.10 and activate it, e.g. with [`miniconda`](https://docs.anaconda.com/free/miniconda/index.html):
 
 ```bash
@@ -127,21 +124,10 @@ conda install ffmpeg -c conda-forge
 >
 > - _[On Linux only]_ Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.
 
-### Install LeRobot 🤗
-
-#### From Source
-
-First, clone the repository and navigate into the directory:
+Install 🤗 LeRobot:
 
 ```bash
-git clone https://github.com/huggingface/lerobot.git
-cd lerobot
-```
-
-Then, install the library in editable mode. This is useful if you plan to contribute to the code.
-
-```bash
-pip install -e .
+pip install lerobot
 ```
 
 > **NOTE:** If you encounter build errors, you may need to install additional dependencies (`cmake`, `build-essential`, and `ffmpeg libs`). On Linux, run:
@@ -159,34 +145,6 @@ For instance, to install 🤗 LeRobot with aloha and pusht, use:
 pip install -e ".[aloha, pusht]"
 ```
 
-### Installation from PyPI
-
-**Core Library:**
-Install the base package with:
-
-```bash
-pip install lerobot
-```
-
-_This installs only the default dependencies._
-
-**Extra Features:**
-To install additional functionality, use one of the following:
-
-```bash
-pip install 'lerobot[all]'          # All available features
-pip install 'lerobot[aloha,pusht]'  # Specific features (Aloha & Pusht)
-pip install 'lerobot[feetech]'      # Feetech motor support
-```
-
-_Replace `[...]` with your desired features._
-
-**Available Tags:**
-For a full list of optional dependencies, see:
-https://pypi.org/project/lerobot/
-
-### Weights & Biases
-
 To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiment tracking, log in with
 
 ```bash
@@ -353,7 +311,7 @@ If you want, you can cite this work with:
 
 ```bibtex
 @misc{cadene2024lerobot,
-    author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Palma, Steven and Kooijmans, Pepijn and Aractingi, Michel and Shukor, Mustafa and Aubakirova, Dana and Russi, Martino and Capuano, Francesco and Pascal, Caroline and Choghari, Jade and Moss, Jess and Wolf, Thomas},
+    author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Palma, Steven and Kooijmans, Pepijn and Aractingi, Michel and Shukor, Mustafa and Aubakirova, Dana and Russi, Martino and Capuano, Francesco and Pascale, Caroline and Choghari, Jade and Moss, Jess and Wolf, Thomas},
     title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch},
     howpublished = "\url{https://github.com/huggingface/lerobot}",
     year = {2024}

docs/source/il_robots.mdx

@@ -294,7 +294,7 @@ dataset.push_to_hub()
 
 #### Dataset upload
 
-Locally, your dataset is stored in this folder: `~/.cache/huggingface/lerobot/{repo-id}`. At the end of data recording, your dataset will be uploaded on your Hugging Face page (e.g. `https://huggingface.co/datasets/${HF_USER}/so101_test`) that you can obtain by running:
+Locally, your dataset is stored in this folder: `~/.cache/huggingface/lerobot/{repo-id}`. At the end of data recording, your dataset will be uploaded on your Hugging Face page (e.g. https://huggingface.co/datasets/cadene/so101_test) that you can obtain by running:
 
 ```bash
 echo https://huggingface.co/datasets/${HF_USER}/so101_test
@@ -428,7 +428,7 @@ Your robot should replicate movements similar to those you recorded. For example
 
 ## Train a policy
 
-To train a policy to control your robot, use the [`python -m lerobot.scripts.train`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
+To train a policy to control your robot, use the [`python -m lerobot.scripts.train`](../src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
 
 ```bash
 python -m lerobot.scripts.train \
@@ -444,7 +444,7 @@ python -m lerobot.scripts.train \
 Let's explain the command:
 
 1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/so101_test`.
-2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
+2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../src/lerobot/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
 3. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
 4. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
 

docs/source/il_sim.mdx

@@ -96,7 +96,7 @@ If you uploaded your dataset to the hub you can [visualize your dataset online](
 
 ## Train a policy
 
-To train a policy to control your robot, use the [`python -m lerobot.scripts.train`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
+To train a policy to control your robot, use the [`python -m lerobot.scripts.train`](../src/lerobot/scripts/train.py) script. A few arguments are required. Here is an example command:
 
 ```bash
 python -m lerobot.scripts.train \
@@ -111,7 +111,7 @@ python -m lerobot.scripts.train \
 Let's explain the command:
 
 1. We provided the dataset as argument with `--dataset.repo_id=${HF_USER}/il_gym`.
-2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
+2. We provided the policy with `policy.type=act`. This loads configurations from [`configuration_act.py`](../src/lerobot/policies/act/configuration_act.py). Importantly, this policy will automatically adapt to the number of motor states, motor actions and cameras of your robot (e.g. `laptop` and `phone`) which have been saved in your dataset.
 3. We provided `policy.device=cuda` since we are training on a Nvidia GPU, but you could use `policy.device=mps` to train on Apple silicon.
 4. We provided `wandb.enable=true` to use [Weights and Biases](https://docs.wandb.ai/quickstart) for visualizing training plots. This is optional but if you use it, make sure you are logged in by running `wandb login`.
 

docs/source/installation.mdx

@@ -1,6 +1,15 @@
 # Installation
 
-## Environment Setup
+## Install LeRobot
+
+Currently only available from source.
+
+Download our source code:
+
+```bash
+git clone https://github.com/huggingface/lerobot.git
+cd lerobot
+```
 
 Create a virtual environment with Python 3.10, using [`Miniconda`](https://docs.anaconda.com/miniconda/install/#quick-command-line-install)
 
@@ -31,49 +40,12 @@ conda install ffmpeg -c conda-forge
 >
 > - _[On Linux only]_ If you want to bring your own ffmpeg: Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.
 
-## Install LeRobot 🤗
-
-### From Source
-
-First, clone the repository and navigate into the directory:
-
-```bash
-git clone https://github.com/huggingface/lerobot.git
-cd lerobot
-```
-
-Then, install the library in editable mode. This is useful if you plan to contribute to the code.
+Install 🤗 LeRobot:
 
 ```bash
 pip install -e .
 ```
 
-### Installation from PyPI
-
-**Core Library:**
-Install the base package with:
-
-```bash
-pip install lerobot
-```
-
-_This installs only the default dependencies._
-
-**Extra Features:**
-To install additional functionality, use one of the following:
-
-```bash
-pip install 'lerobot[all]'          # All available features
-pip install 'lerobot[aloha,pusht]'  # Specific features (Aloha & Pusht)
-pip install 'lerobot[feetech]'      # Feetech motor support
-```
-
-_Replace `[...]` with your desired features._
-
-**Available Tags:**
-For a full list of optional dependencies, see:
-https://pypi.org/project/lerobot/
-
 ### Troubleshooting
 
 If you encounter build errors, you may need to install additional dependencies: `cmake`, `build-essential`, and `ffmpeg libs`.

pyproject.toml

@@ -25,7 +25,7 @@ discord = "https://discord.gg/s3KuuzsPFb"
 
 [project]
 name = "lerobot"
-version = "0.3.3"
+version = "0.3.2"
 description = "🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch"
 readme = "README.md"
 license = { text = "Apache-2.0" }
@@ -68,16 +68,15 @@ dependencies = [
     "einops>=0.8.0",
     "opencv-python-headless>=4.9.0",
     "av>=14.2.0",
+    "torch>=2.2.1",
+    "torchcodec>=0.2.1; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')",
+    "torchvision>=0.21.0",
     "jsonlines>=4.0.0",
     "packaging>=24.2",
     "pynput>=1.7.7",
     "pyserial>=3.5",
     "wandb>=0.20.0",
 
-    "torch>=2.2.1,<2.8.0", # TODO: Bumb dependency
-    "torchcodec>=0.2.1,<0.6.0; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')", # TODO: Bumb dependency
-    "torchvision>=0.21.0,<0.23.0", # TODO: Bumb dependency
-
     "draccus==0.10.0", # TODO: Remove ==
     "gymnasium>=0.29.1,<1.0.0", # TODO: Bumb dependency
     "rerun-sdk>=0.21.0,<0.23.0", # TODO: Bumb dependency

src/lerobot/configs/policies.py

@@ -27,7 +27,6 @@ from huggingface_hub.constants import CONFIG_NAME
 from huggingface_hub.errors import HfHubHTTPError
 
 from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
-from lerobot.constants import ACTION, OBS_STATE
 from lerobot.optim.optimizers import OptimizerConfig
 from lerobot.optim.schedulers import LRSchedulerConfig
 from lerobot.utils.hub import HubMixin
@@ -120,8 +119,8 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):
 
     @property
     def robot_state_feature(self) -> PolicyFeature | None:
-        for ft_name, ft in self.input_features.items():
-            if ft.type is FeatureType.STATE and ft_name == OBS_STATE:
+        for _, ft in self.input_features.items():
+            if ft.type is FeatureType.STATE:
                 return ft
         return None
 
@@ -138,8 +137,8 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):
 
     @property
     def action_feature(self) -> PolicyFeature | None:
-        for ft_name, ft in self.output_features.items():
-            if ft.type is FeatureType.ACTION and ft_name == ACTION:
+        for _, ft in self.output_features.items():
+            if ft.type is FeatureType.ACTION:
                 return ft
         return None
 

src/lerobot/policies/normalize.py

@@ -24,6 +24,7 @@ def create_stats_buffers(
     features: dict[str, PolicyFeature],
     norm_map: dict[str, NormalizationMode],
     stats: dict[str, dict[str, Tensor]] | None = None,
+    dtype: torch.dtype = torch.float32,
 ) -> dict[str, dict[str, nn.ParameterDict]]:
     """
     Create buffers per modality (e.g. "observation.image", "action") containing their mean, std, min, max
@@ -60,8 +61,8 @@ def create_stats_buffers(
 
         buffer = {}
         if norm_mode is NormalizationMode.MEAN_STD:
-            mean = torch.ones(shape, dtype=torch.float32) * torch.inf
-            std = torch.ones(shape, dtype=torch.float32) * torch.inf
+            mean = torch.ones(shape, dtype=dtype) * torch.inf
+            std = torch.ones(shape, dtype=dtype) * torch.inf
             buffer = nn.ParameterDict(
                 {
                     "mean": nn.Parameter(mean, requires_grad=False),
@@ -69,8 +70,8 @@ def create_stats_buffers(
                 }
             )
         elif norm_mode is NormalizationMode.MIN_MAX:
-            min = torch.ones(shape, dtype=torch.float32) * torch.inf
-            max = torch.ones(shape, dtype=torch.float32) * torch.inf
+            min = torch.ones(shape, dtype=dtype) * torch.inf
+            max = torch.ones(shape, dtype=dtype) * torch.inf
             buffer = nn.ParameterDict(
                 {
                     "min": nn.Parameter(min, requires_grad=False),
@@ -82,22 +83,22 @@ def create_stats_buffers(
         if stats:
             if isinstance(stats[key]["mean"], np.ndarray):
                 if norm_mode is NormalizationMode.MEAN_STD:
-                    buffer["mean"].data = torch.from_numpy(stats[key]["mean"]).to(dtype=torch.float32)
-                    buffer["std"].data = torch.from_numpy(stats[key]["std"]).to(dtype=torch.float32)
+                    buffer["mean"].data = torch.from_numpy(stats[key]["mean"]).to(dtype=dtype)
+                    buffer["std"].data = torch.from_numpy(stats[key]["std"]).to(dtype=dtype)
                 elif norm_mode is NormalizationMode.MIN_MAX:
-                    buffer["min"].data = torch.from_numpy(stats[key]["min"]).to(dtype=torch.float32)
-                    buffer["max"].data = torch.from_numpy(stats[key]["max"]).to(dtype=torch.float32)
+                    buffer["min"].data = torch.from_numpy(stats[key]["min"]).to(dtype=dtype)
+                    buffer["max"].data = torch.from_numpy(stats[key]["max"]).to(dtype=dtype)
             elif isinstance(stats[key]["mean"], torch.Tensor):
                 # Note: The clone is needed to make sure that the logic in save_pretrained doesn't see duplicated
                 # tensors anywhere (for example, when we use the same stats for normalization and
                 # unnormalization). See the logic here
                 # https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/py_src/safetensors/torch.py#L97.
                 if norm_mode is NormalizationMode.MEAN_STD:
-                    buffer["mean"].data = stats[key]["mean"].clone().to(dtype=torch.float32)
-                    buffer["std"].data = stats[key]["std"].clone().to(dtype=torch.float32)
+                    buffer["mean"].data = stats[key]["mean"].clone().to(dtype=dtype)
+                    buffer["std"].data = stats[key]["std"].clone().to(dtype=dtype)
                 elif norm_mode is NormalizationMode.MIN_MAX:
-                    buffer["min"].data = stats[key]["min"].clone().to(dtype=torch.float32)
-                    buffer["max"].data = stats[key]["max"].clone().to(dtype=torch.float32)
+                    buffer["min"].data = stats[key]["min"].clone().to(dtype=dtype)
+                    buffer["max"].data = stats[key]["max"].clone().to(dtype=dtype)
             else:
                 type_ = type(stats[key]["mean"])
                 raise ValueError(f"np.ndarray or torch.Tensor expected, but type is '{type_}' instead.")
@@ -121,6 +122,7 @@ class Normalize(nn.Module):
         features: dict[str, PolicyFeature],
         norm_map: dict[str, NormalizationMode],
         stats: dict[str, dict[str, Tensor]] | None = None,
+        dtype: torch.dtype = torch.float32,
     ):
         """
         Args:
@@ -144,7 +146,7 @@ class Normalize(nn.Module):
         self.features = features
         self.norm_map = norm_map
         self.stats = stats
-        stats_buffers = create_stats_buffers(features, norm_map, stats)
+        stats_buffers = create_stats_buffers(features, norm_map, stats, dtype)
         for key, buffer in stats_buffers.items():
             setattr(self, "buffer_" + key.replace(".", "_"), buffer)
 
@@ -195,6 +197,7 @@ class Unnormalize(nn.Module):
         features: dict[str, PolicyFeature],
         norm_map: dict[str, NormalizationMode],
         stats: dict[str, dict[str, Tensor]] | None = None,
+        dtype: torch.dtype = torch.float32,
     ):
         """
         Args:
@@ -219,7 +222,7 @@ class Unnormalize(nn.Module):
         self.norm_map = norm_map
         self.stats = stats
         # `self.buffer_observation_state["mean"]` contains `torch.tensor(state_dim)`
-        stats_buffers = create_stats_buffers(features, norm_map, stats)
+        stats_buffers = create_stats_buffers(features, norm_map, stats, dtype)
         for key, buffer in stats_buffers.items():
             setattr(self, "buffer_" + key.replace(".", "_"), buffer)
 
@@ -262,6 +265,7 @@ def _initialize_stats_buffers(
     features: dict[str, PolicyFeature],
     norm_map: dict[str, NormalizationMode],
     stats: dict[str, dict[str, Tensor]] | None = None,
+    dtype: torch.dtype = torch.float32,
 ) -> None:
     """Register statistics buffers (mean/std or min/max) on the given *module*.
 
@@ -282,8 +286,8 @@ def _initialize_stats_buffers(
         prefix = key.replace(".", "_")
 
         if norm_mode is NormalizationMode.MEAN_STD:
-            mean = torch.full(shape, torch.inf, dtype=torch.float32)
-            std = torch.full(shape, torch.inf, dtype=torch.float32)
+            mean = torch.full(shape, torch.inf, dtype=dtype)
+            std = torch.full(shape, torch.inf, dtype=dtype)
 
             if stats and key in stats and "mean" in stats[key] and "std" in stats[key]:
                 mean_data = stats[key]["mean"]
@@ -293,8 +297,8 @@ def _initialize_stats_buffers(
                     # tensors anywhere (for example, when we use the same stats for normalization and
                     # unnormalization). See the logic here
                     # https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/py_src/safetensors/torch.py#L97.
-                    mean = mean_data.clone().to(dtype=torch.float32)
-                    std = std_data.clone().to(dtype=torch.float32)
+                    mean = mean_data.clone().to(dtype=dtype)
+                    std = std_data.clone().to(dtype=dtype)
                 else:
                     raise ValueError(f"Unsupported stats type for key '{key}' (expected ndarray or Tensor).")
 
@@ -303,15 +307,15 @@ def _initialize_stats_buffers(
             continue
 
         if norm_mode is NormalizationMode.MIN_MAX:
-            min_val = torch.full(shape, torch.inf, dtype=torch.float32)
-            max_val = torch.full(shape, torch.inf, dtype=torch.float32)
+            min_val = torch.full(shape, torch.inf, dtype=dtype)
+            max_val = torch.full(shape, torch.inf, dtype=dtype)
 
             if stats and key in stats and "min" in stats[key] and "max" in stats[key]:
                 min_data = stats[key]["min"]
                 max_data = stats[key]["max"]
                 if isinstance(min_data, torch.Tensor):
-                    min_val = min_data.clone().to(dtype=torch.float32)
-                    max_val = max_data.clone().to(dtype=torch.float32)
+                    min_val = min_data.clone().to(dtype=dtype)
+                    max_val = max_data.clone().to(dtype=dtype)
                 else:
                     raise ValueError(f"Unsupported stats type for key '{key}' (expected ndarray or Tensor).")
 
@@ -330,12 +334,13 @@ class NormalizeBuffer(nn.Module):
         features: dict[str, PolicyFeature],
         norm_map: dict[str, NormalizationMode],
         stats: dict[str, dict[str, Tensor]] | None = None,
+        dtype: torch.dtype = torch.float32,
     ):
         super().__init__()
         self.features = features
         self.norm_map = norm_map
 
-        _initialize_stats_buffers(self, features, norm_map, stats)
+        _initialize_stats_buffers(self, features, norm_map, stats, dtype)
 
     def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
         batch = dict(batch)
@@ -379,12 +384,13 @@ class UnnormalizeBuffer(nn.Module):
         features: dict[str, PolicyFeature],
         norm_map: dict[str, NormalizationMode],
         stats: dict[str, dict[str, Tensor]] | None = None,
+        dtype: torch.dtype = torch.float32,
     ):
         super().__init__()
         self.features = features
         self.norm_map = norm_map
 
-        _initialize_stats_buffers(self, features, norm_map, stats)
+        _initialize_stats_buffers(self, features, norm_map, stats, dtype)
 
     def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
         # batch = dict(batch)

src/lerobot/policies/smolvla/modeling_smolvla.py

@@ -673,19 +673,19 @@ class VLAFlowMatching(nn.Module):
         for params in self.state_proj.parameters():
             params.requires_grad = self.config.train_state_proj
 
-    def sample_noise(self, shape, device):
+    def sample_noise(self, shape, device, dtype):
         noise = torch.normal(
             mean=0.0,
             std=1.0,
             size=shape,
-            dtype=torch.float32,
+            dtype=dtype,
             device=device,
         )
         return noise
 
-    def sample_time(self, bsize, device):
+    def sample_time(self, bsize, device, dtype):
         beta_dist = torch.distributions.Beta(concentration1=1.5, concentration0=1.0)
-        time_beta = beta_dist.sample((bsize,)).to(device=device, dtype=torch.float32)
+        time_beta = beta_dist.sample((bsize,)).to(device=device, dtype=dtype)
         time = time_beta * 0.999 + 0.001
         return time
 
@@ -831,10 +831,10 @@ class VLAFlowMatching(nn.Module):
     ) -> Tensor:
         """Do a full training forward pass and compute the loss (batch_size x num_steps x num_motors)"""
         if noise is None:
-            noise = self.sample_noise(actions.shape, actions.device)
+            noise = self.sample_noise(actions.shape, actions.device, actions.dtype)
 
         if time is None:
-            time = self.sample_time(actions.shape[0], actions.device)
+            time = self.sample_time(actions.shape[0], actions.device, actions.dtype)
 
         time_expanded = time[:, None, None]
         x_t = time_expanded * noise + (1 - time_expanded) * actions
@@ -868,10 +868,11 @@ class VLAFlowMatching(nn.Module):
         """Do a full inference forward and compute the action (batch_size x num_steps x num_motors)"""
         bsize = state.shape[0]
         device = state.device
+        dtype = state.dtype
 
         if noise is None:
             actions_shape = (bsize, self.config.chunk_size, self.config.max_action_dim)
-            noise = self.sample_noise(actions_shape, device)
+            noise = self.sample_noise(actions_shape, device, dtype)
 
         prefix_embs, prefix_pad_masks, prefix_att_masks = self.embed_prefix(
             images, img_masks, lang_tokens, lang_masks, state=state
@@ -888,18 +889,13 @@ class VLAFlowMatching(nn.Module):
             fill_kv_cache=True,
         )
         dt = -1.0 / self.config.num_steps
-        dt = torch.tensor(dt, dtype=torch.float32, device=device)
+        dt = torch.tensor(dt, dtype=dtype, device=device)
 
         x_t = noise
-        time = torch.tensor(1.0, dtype=torch.float32, device=device)
+        time = torch.tensor(1.0, dtype=dtype, device=device)
         while time >= -dt / 2:
             expanded_time = time.expand(bsize)
-            v_t = self.denoise_step(
-                prefix_pad_masks,
-                past_key_values,
-                x_t,
-                expanded_time,
-            )
+            v_t = self.denoise_step(prefix_pad_masks, past_key_values, x_t, expanded_time, dtype)
             # Euler step
             x_t += dt * v_t
             time += dt
@@ -911,6 +907,7 @@ class VLAFlowMatching(nn.Module):
         past_key_values,
         x_t,
         timestep,
+        dtype,
     ):
         """Apply one denoising step of the noise `x_t` at a given timestep."""
         suffix_embs, suffix_pad_masks, suffix_att_masks = self.embed_suffix(x_t, timestep)
@@ -936,6 +933,6 @@ class VLAFlowMatching(nn.Module):
         )
         suffix_out = outputs_embeds[1]
         suffix_out = suffix_out[:, -self.config.chunk_size :]
-        suffix_out = suffix_out.to(dtype=torch.float32)
+        suffix_out = suffix_out.to(dtype=dtype)
         v_t = self.action_out_proj(suffix_out)
         return v_t

src/lerobot/processor/__init__.py

@@ -1,54 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from .device_processor import DeviceProcessor
-from .normalize_processor import NormalizerProcessor, UnnormalizerProcessor
-from .observation_processor import VanillaObservationProcessor
-from .pipeline import (
-    ActionProcessor,
-    DoneProcessor,
-    EnvTransition,
-    IdentityProcessor,
-    InfoProcessor,
-    ObservationProcessor,
-    ProcessorStep,
-    ProcessorStepRegistry,
-    RewardProcessor,
-    RobotProcessor,
-    TransitionKey,
-    TruncatedProcessor,
-)
-from .rename_processor import RenameProcessor
-
-__all__ = [
-    "ActionProcessor",
-    "DeviceProcessor",
-    "DoneProcessor",
-    "EnvTransition",
-    "IdentityProcessor",
-    "InfoProcessor",
-    "NormalizerProcessor",
-    "UnnormalizerProcessor",
-    "ObservationProcessor",
-    "ProcessorStep",
-    "ProcessorStepRegistry",
-    "RenameProcessor",
-    "RewardProcessor",
-    "RobotProcessor",
-    "TransitionKey",
-    "TruncatedProcessor",
-    "VanillaObservationProcessor",
-]

src/lerobot/processor/device_processor.py

@@ -1,82 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from dataclasses import dataclass
-from typing import Any
-
-import torch
-
-from lerobot.configs.types import PolicyFeature
-from lerobot.processor.pipeline import EnvTransition, TransitionKey
-from lerobot.utils.utils import get_safe_torch_device
-
-
-@dataclass
-class DeviceProcessor:
-    """Processes transitions by moving tensors to the specified device.
-
-    This processor ensures that all tensors in the transition are moved to the
-    specified device (CPU or GPU) before they are returned.
-    """
-
-    device: torch.device = "cpu"
-
-    def __post_init__(self):
-        self.device = get_safe_torch_device(self.device)
-        self.non_blocking = "cuda" in str(self.device)
-
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        # Create a copy of the transition
-        new_transition = transition.copy()
-
-        # Process observation tensors
-        observation = transition.get(TransitionKey.OBSERVATION)
-        if observation is not None:
-            new_observation = {
-                k: v.to(self.device, non_blocking=self.non_blocking) if isinstance(v, torch.Tensor) else v
-                for k, v in observation.items()
-            }
-            new_transition[TransitionKey.OBSERVATION] = new_observation
-
-        # Process action tensor
-        action = transition.get(TransitionKey.ACTION)
-        if action is not None and isinstance(action, torch.Tensor):
-            new_transition[TransitionKey.ACTION] = action.to(self.device, non_blocking=self.non_blocking)
-
-        # Process reward tensor
-        reward = transition.get(TransitionKey.REWARD)
-        if reward is not None and isinstance(reward, torch.Tensor):
-            new_transition[TransitionKey.REWARD] = reward.to(self.device, non_blocking=self.non_blocking)
-
-        # Process done tensor
-        done = transition.get(TransitionKey.DONE)
-        if done is not None and isinstance(done, torch.Tensor):
-            new_transition[TransitionKey.DONE] = done.to(self.device, non_blocking=self.non_blocking)
-
-        # Process truncated tensor
-        truncated = transition.get(TransitionKey.TRUNCATED)
-        if truncated is not None and isinstance(truncated, torch.Tensor):
-            new_transition[TransitionKey.TRUNCATED] = truncated.to(
-                self.device, non_blocking=self.non_blocking
-            )
-
-        return new_transition
-
-    def get_config(self) -> dict[str, Any]:
-        """Return configuration for serialization."""
-        return {"device": self.device}
-
-    def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
-        return features

src/lerobot/processor/normalize_processor.py

@@ -1,331 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import Mapping
-from dataclasses import dataclass, field
-from typing import Any
-
-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, TransitionKey
-
-
-def _convert_stats_to_tensors(stats: dict[str, dict[str, Any]]) -> dict[str, dict[str, Tensor]]:
-    """Convert numpy arrays and other types to torch tensors."""
-    tensor_stats: dict[str, dict[str, Tensor]] = {}
-    for key, sub in stats.items():
-        tensor_stats[key] = {}
-        for stat_name, value in sub.items():
-            if isinstance(value, np.ndarray):
-                tensor_val = torch.from_numpy(value.astype(np.float32))
-            elif isinstance(value, torch.Tensor):
-                tensor_val = value.to(dtype=torch.float32)
-            elif isinstance(value, (int, float, list, tuple)):
-                tensor_val = torch.tensor(value, dtype=torch.float32)
-            else:
-                raise TypeError(f"Unsupported type for stats['{key}']['{stat_name}']: {type(value)}")
-            tensor_stats[key][stat_name] = tensor_val
-    return tensor_stats
-
-
-@dataclass
-@ProcessorStepRegistry.register(name="normalizer_processor")
-class NormalizerProcessor:
-    """Normalizes observations and actions in a single processor step.
-
-    This processor handles normalization of both observation and action tensors
-    using either mean/std normalization or min/max scaling to a [-1, 1] range.
-
-    For each tensor key in the stats dictionary, the processor will:
-    - Use mean/std normalization if those statistics are provided: (x - mean) / std
-    - Use min/max scaling if those statistics are provided: 2 * (x - min) / (max - min) - 1
-
-    The processor can be configured to normalize only specific keys by setting
-    the normalize_keys parameter.
-    """
-
-    # 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: dict[str, dict[str, Any]] | None = 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: set[str] | None = None
-
-    eps: float = 1e-8
-
-    _tensor_stats: dict[str, dict[str, Tensor]] = field(default_factory=dict, init=False, repr=False)
-
-    @classmethod
-    def from_lerobot_dataset(
-        cls,
-        dataset: LeRobotDataset,
-        features: dict[str, PolicyFeature],
-        norm_map: dict[FeatureType, NormalizationMode],
-        *,
-        normalize_keys: set[str] | None = None,
-        eps: float = 1e-8,
-    ) -> 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):
-        # Handle deserialization from JSON config
-        if self.features and isinstance(list(self.features.values())[0], dict):
-            # Features came from JSON - need to reconstruct PolicyFeature objects
-            reconstructed_features = {}
-            for key, ft_dict in self.features.items():
-                reconstructed_features[key] = PolicyFeature(
-                    type=FeatureType(ft_dict["type"]), shape=tuple(ft_dict["shape"])
-                )
-            self.features = reconstructed_features
-
-        if self.norm_map and isinstance(list(self.norm_map.keys())[0], str):
-            # norm_map came from JSON - need to reconstruct enum keys and values
-            reconstructed_norm_map = {}
-            for ft_type_str, norm_mode_str in self.norm_map.items():
-                reconstructed_norm_map[FeatureType(ft_type_str)] = NormalizationMode(norm_mode_str)
-            self.norm_map = reconstructed_norm_map
-
-        # 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
-
-        # 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:
-                continue
-
-            orig_val = processed[key]
-            tensor = (
-                orig_val.to(dtype=torch.float32)
-                if isinstance(orig_val, torch.Tensor)
-                else torch.as_tensor(orig_val, dtype=torch.float32)
-            )
-            stats = {k: v.to(tensor.device) for k, v in self._tensor_stats[key].items()}
-
-            if "mean" in stats and "std" in stats:
-                mean, std = stats["mean"], stats["std"]
-                processed[key] = (tensor - mean) / (std + self.eps)
-            elif "min" in stats and "max" in stats:
-                min_val, max_val = stats["min"], stats["max"]
-                processed[key] = 2 * (tensor - min_val) / (max_val - min_val + self.eps) - 1
-        return processed
-
-    def _normalize_action(self, action):
-        if action is None or "action" not in self._tensor_stats:
-            return action
-
-        tensor = (
-            action.to(dtype=torch.float32)
-            if isinstance(action, torch.Tensor)
-            else torch.as_tensor(action, dtype=torch.float32)
-        )
-        stats = {k: v.to(tensor.device) for k, v in self._tensor_stats["action"].items()}
-        if "mean" in stats and "std" in stats:
-            mean, std = stats["mean"], stats["std"]
-            return (tensor - mean) / (std + self.eps)
-        if "min" in stats and "max" in stats:
-            min_val, max_val = stats["min"], stats["max"]
-            return 2 * (tensor - min_val) / (max_val - min_val + self.eps) - 1
-        raise ValueError("Action stats must contain either ('mean','std') or ('min','max')")
-
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        observation = self._normalize_obs(transition.get(TransitionKey.OBSERVATION))
-        action = self._normalize_action(transition.get(TransitionKey.ACTION))
-
-        # Create a new transition with normalized values
-        new_transition = transition.copy()
-        new_transition[TransitionKey.OBSERVATION] = observation
-        new_transition[TransitionKey.ACTION] = action
-        return new_transition
-
-    def get_config(self) -> dict[str, Any]:
-        config = {
-            "eps": self.eps,
-            "features": {
-                key: {"type": ft.type.value, "shape": ft.shape} for key, ft in self.features.items()
-            },
-            "norm_map": {ft_type.value: norm_mode.value for ft_type, norm_mode in self.norm_map.items()},
-        }
-        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 = {}
-        for key, sub in self._tensor_stats.items():
-            for stat_name, tensor in sub.items():
-                flat[f"{key}.{stat_name}"] = tensor
-        return flat
-
-    def load_state_dict(self, state: Mapping[str, Tensor]) -> None:
-        self._tensor_stats.clear()
-        for flat_key, tensor in state.items():
-            key, stat_name = flat_key.rsplit(".", 1)
-            self._tensor_stats.setdefault(key, {})[stat_name] = tensor
-
-    def reset(self):
-        pass
-
-    def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
-        return features
-
-
-@dataclass
-@ProcessorStepRegistry.register(name="unnormalizer_processor")
-class UnnormalizerProcessor:
-    """Inverse normalisation for observations and actions.
-
-    Exactly mirrors :class:`NormalizerProcessor` but applies the inverse
-    transform.
-    """
-
-    features: dict[str, PolicyFeature]
-    norm_map: dict[FeatureType, NormalizationMode]
-    stats: dict[str, dict[str, Any]] | None = None
-
-    _tensor_stats: dict[str, dict[str, Tensor]] = field(default_factory=dict, init=False, repr=False)
-
-    @classmethod
-    def from_lerobot_dataset(
-        cls,
-        dataset: LeRobotDataset,
-        features: dict[str, PolicyFeature],
-        norm_map: dict[FeatureType, NormalizationMode],
-    ) -> UnnormalizerProcessor:
-        return cls(features=features, norm_map=norm_map, stats=dataset.meta.stats)
-
-    def __post_init__(self):
-        # Handle deserialization from JSON config
-        if self.features and isinstance(list(self.features.values())[0], dict):
-            # Features came from JSON - need to reconstruct PolicyFeature objects
-            reconstructed_features = {}
-            for key, ft_dict in self.features.items():
-                reconstructed_features[key] = PolicyFeature(
-                    type=FeatureType(ft_dict["type"]), shape=tuple(ft_dict["shape"])
-                )
-            self.features = reconstructed_features
-
-        if self.norm_map and isinstance(list(self.norm_map.keys())[0], str):
-            # norm_map came from JSON - need to reconstruct enum keys and values
-            reconstructed_norm_map = {}
-            for ft_type_str, norm_mode_str in self.norm_map.items():
-                reconstructed_norm_map[FeatureType(ft_type_str)] = NormalizationMode(norm_mode_str)
-            self.norm_map = reconstructed_norm_map
-
-        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 = [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:
-                continue
-            orig_val = processed[key]
-            tensor = (
-                orig_val.to(dtype=torch.float32)
-                if isinstance(orig_val, torch.Tensor)
-                else torch.as_tensor(orig_val, dtype=torch.float32)
-            )
-            stats = {k: v.to(tensor.device) for k, v in self._tensor_stats[key].items()}
-            if "mean" in stats and "std" in stats:
-                mean, std = stats["mean"], stats["std"]
-                processed[key] = tensor * std + mean
-            elif "min" in stats and "max" in stats:
-                min_val, max_val = stats["min"], stats["max"]
-                processed[key] = (tensor + 1) / 2 * (max_val - min_val) + min_val
-        return processed
-
-    def _unnormalize_action(self, action):
-        if action is None or "action" not in self._tensor_stats:
-            return action
-        tensor = (
-            action.to(dtype=torch.float32)
-            if isinstance(action, torch.Tensor)
-            else torch.as_tensor(action, dtype=torch.float32)
-        )
-        stats = {k: v.to(tensor.device) for k, v in self._tensor_stats["action"].items()}
-        if "mean" in stats and "std" in stats:
-            mean, std = stats["mean"], stats["std"]
-            return tensor * std + mean
-        if "min" in stats and "max" in stats:
-            min_val, max_val = stats["min"], stats["max"]
-            return (tensor + 1) / 2 * (max_val - min_val) + min_val
-        raise ValueError("Action stats must contain either ('mean','std') or ('min','max')")
-
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        observation = self._unnormalize_obs(transition.get(TransitionKey.OBSERVATION))
-        action = self._unnormalize_action(transition.get(TransitionKey.ACTION))
-
-        # Create a new transition with unnormalized values
-        new_transition = transition.copy()
-        new_transition[TransitionKey.OBSERVATION] = observation
-        new_transition[TransitionKey.ACTION] = action
-        return new_transition
-
-    def get_config(self) -> dict[str, Any]:
-        return {
-            "features": {
-                key: {"type": ft.type.value, "shape": ft.shape} for key, ft in self.features.items()
-            },
-            "norm_map": {ft_type.value: norm_mode.value for ft_type, norm_mode in self.norm_map.items()},
-        }
-
-    def state_dict(self) -> dict[str, Tensor]:
-        flat = {}
-        for key, sub in self._tensor_stats.items():
-            for stat_name, tensor in sub.items():
-                flat[f"{key}.{stat_name}"] = tensor
-        return flat
-
-    def load_state_dict(self, state: Mapping[str, Tensor]) -> None:
-        self._tensor_stats.clear()
-        for flat_key, tensor in state.items():
-            key, stat_name = flat_key.rsplit(".", 1)
-            self._tensor_stats.setdefault(key, {})[stat_name] = tensor
-
-    def reset(self):
-        pass
-
-    def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
-        return features

src/lerobot/processor/observation_processor.py

@@ -1,157 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from dataclasses import dataclass
-
-import einops
-import numpy as np
-import torch
-from torch import Tensor
-
-from lerobot.configs.types import PolicyFeature
-from lerobot.constants import OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE
-from lerobot.processor.pipeline import ObservationProcessor, ProcessorStepRegistry
-
-
-@dataclass
-@ProcessorStepRegistry.register(name="observation_processor")
-class VanillaObservationProcessor(ObservationProcessor):
-    """
-    Processes environment observations into the LeRobot format by handling both images and states.
-
-    Image processing:
-        - Converts channel-last (H, W, C) images to channel-first (C, H, W)
-        - Normalizes uint8 images ([0, 255]) to float32 ([0, 1])
-        - Adds a batch dimension if missing
-        - Supports single images and image dictionaries
-
-    State processing:
-        - Maps 'environment_state' to observation.environment_state
-        - Maps 'agent_pos' to observation.state
-        - Converts numpy arrays to tensors
-        - Adds a batch dimension if missing
-    """
-
-    def _process_single_image(self, img: np.ndarray) -> Tensor:
-        """Process a single image array."""
-        # Convert to tensor
-        img_tensor = torch.from_numpy(img)
-
-        # Add batch dimension if needed
-        if img_tensor.ndim == 3:
-            img_tensor = img_tensor.unsqueeze(0)
-
-        # Validate image format
-        _, h, w, c = img_tensor.shape
-        if not (c < h and c < w):
-            raise ValueError(f"Expected channel-last images, but got shape {img_tensor.shape}")
-
-        if img_tensor.dtype != torch.uint8:
-            raise ValueError(f"Expected torch.uint8 images, but got {img_tensor.dtype}")
-
-        # Convert to channel-first format
-        img_tensor = einops.rearrange(img_tensor, "b h w c -> b c h w").contiguous()
-
-        # Convert to float32 and normalize to [0, 1]
-        img_tensor = img_tensor.type(torch.float32) / 255.0
-
-        return img_tensor
-
-    def _process_observation(self, observation):
-        """
-        Processes both image and state observations.
-        """
-
-        processed_obs = observation.copy()
-
-        if "pixels" in processed_obs:
-            pixels = processed_obs.pop("pixels")
-
-            if isinstance(pixels, dict):
-                imgs = {f"{OBS_IMAGES}.{key}": img for key, img in pixels.items()}
-            else:
-                imgs = {OBS_IMAGE: pixels}
-
-            for imgkey, img in imgs.items():
-                processed_obs[imgkey] = self._process_single_image(img)
-
-        if "environment_state" in processed_obs:
-            env_state_np = processed_obs.pop("environment_state")
-            env_state = torch.from_numpy(env_state_np).float()
-            if env_state.dim() == 1:
-                env_state = env_state.unsqueeze(0)
-            processed_obs[OBS_ENV_STATE] = env_state
-
-        if "agent_pos" in processed_obs:
-            agent_pos_np = processed_obs.pop("agent_pos")
-            agent_pos = torch.from_numpy(agent_pos_np).float()
-            if agent_pos.dim() == 1:
-                agent_pos = agent_pos.unsqueeze(0)
-            processed_obs[OBS_STATE] = agent_pos
-
-        return processed_obs
-
-    def observation(self, observation):
-        return self._process_observation(observation)
-
-    def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
-        """Transforms feature keys to a standardized contract.
-
-        This method handles several renaming patterns:
-        - Exact matches (e.g., 'pixels' -> 'OBS_IMAGE').
-        - Prefixed exact matches (e.g., 'observation.pixels' -> 'OBS_IMAGE').
-        - Prefix matches (e.g., 'pixels.cam1' -> 'OBS_IMAGES.cam1').
-        - Prefixed prefix matches (e.g., 'observation.pixels.cam1' -> 'OBS_IMAGES.cam1').
-        - environment_state -> OBS_ENV_STATE,
-        - agent_pos -> OBS_STATE,
-        - observation.environment_state -> OBS_ENV_STATE,
-        - observation.agent_pos -> OBS_STATE
-        """
-        exact_pairs = {
-            "pixels": OBS_IMAGE,
-            "environment_state": OBS_ENV_STATE,
-            "agent_pos": OBS_STATE,
-        }
-
-        prefix_pairs = {
-            "pixels.": f"{OBS_IMAGES}.",
-        }
-
-        for key in list(features.keys()):
-            matched_prefix = False
-            for old_prefix, new_prefix in prefix_pairs.items():
-                prefixed_old = f"observation.{old_prefix}"
-                if key.startswith(prefixed_old):
-                    suffix = key[len(prefixed_old) :]
-                    features[f"{new_prefix}{suffix}"] = features.pop(key)
-                    matched_prefix = True
-                    break
-
-                if key.startswith(old_prefix):
-                    suffix = key[len(old_prefix) :]
-                    features[f"{new_prefix}{suffix}"] = features.pop(key)
-                    matched_prefix = True
-                    break
-
-            if matched_prefix:
-                continue
-
-            for old, new in exact_pairs.items():
-                if key == old or key == f"observation.{old}":
-                    if key in features:
-                        features[new] = features.pop(key)
-                        break
-
-        return features

src/lerobot/processor/rename_processor.py

@@ -1,51 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from dataclasses import dataclass, field
-from typing import Any
-
-from lerobot.configs.types import PolicyFeature
-from lerobot.processor.pipeline import (
-    ObservationProcessor,
-    ProcessorStepRegistry,
-)
-
-
-@dataclass
-@ProcessorStepRegistry.register(name="rename_processor")
-class RenameProcessor(ObservationProcessor):
-    """Rename processor that renames keys in the observation."""
-
-    rename_map: dict[str, str] = field(default_factory=dict)
-
-    def observation(self, observation):
-        processed_obs = {}
-        for key, value in observation.items():
-            if key in self.rename_map:
-                processed_obs[self.rename_map[key]] = value
-            else:
-                processed_obs[key] = value
-
-        return processed_obs
-
-    def get_config(self) -> dict[str, Any]:
-        return {"rename_map": self.rename_map}
-
-    def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
-        """Transforms:
-        - Each key in the observation that appears in `rename_map` is renamed to its value.
-        - Keys not in `rename_map` remain unchanged.
-        """
-        return {self.rename_map.get(k, k): v for k, v in features.items()}

tests/conftest.py

@@ -19,7 +19,6 @@ import traceback
 import pytest
 from serial import SerialException
 
-from lerobot.configs.types import FeatureType, PolicyFeature
 from tests.utils import DEVICE
 
 # Import fixture modules as plugins
@@ -70,19 +69,3 @@ def patch_builtins_input(monkeypatch):
             print(text)
 
     monkeypatch.setattr("builtins.input", print_text)
-
-
-@pytest.fixture
-def policy_feature_factory():
-    """PolicyFeature factory"""
-
-    def _pf(ft: FeatureType, shape: tuple[int, ...]) -> PolicyFeature:
-        return PolicyFeature(type=ft, shape=shape)
-
-    return _pf
-
-
-def assert_contract_is_typed(features: dict[str, PolicyFeature]) -> None:
-    assert isinstance(features, dict)
-    assert all(isinstance(k, str) for k in features.keys())
-    assert all(isinstance(v, PolicyFeature) for v in features.values())

tests/policies/test_policies.py

@@ -27,13 +27,11 @@ from lerobot import available_policies
 from lerobot.configs.default import DatasetConfig
 from lerobot.configs.train import TrainPipelineConfig
 from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
-from lerobot.constants import ACTION, OBS_STATE
 from lerobot.datasets.factory import make_dataset
 from lerobot.datasets.utils import cycle, dataset_to_policy_features
 from lerobot.envs.factory import make_env, make_env_config
 from lerobot.envs.utils import preprocess_observation
 from lerobot.optim.factory import make_optimizer_and_scheduler
-from lerobot.policies.act.configuration_act import ACTConfig
 from lerobot.policies.act.modeling_act import ACTTemporalEnsembler
 from lerobot.policies.factory import (
     get_policy_class,
@@ -365,54 +363,6 @@ def test_normalize(insert_temporal_dim):
     unnormalize(output_batch)
 
 
-@pytest.mark.parametrize("multikey", [True, False])
-def test_multikey_construction(multikey: bool):
-    """
-    Asserts that multiple keys with type State/Action are correctly processed by the policy constructor,
-    preventing erroneous creation of the policy object.
-    """
-    input_features = {
-        "observation.state": PolicyFeature(
-            type=FeatureType.STATE,
-            shape=(10,),
-        ),
-    }
-    output_features = {
-        "action": PolicyFeature(
-            type=FeatureType.ACTION,
-            shape=(5,),
-        ),
-    }
-
-    if multikey:
-        """Simulates the complete state/action is constructed from more granular multiple
-        keys, of the same type as the overall state/action"""
-        input_features = {}
-        input_features["observation.state.subset1"] = PolicyFeature(type=FeatureType.STATE, shape=(5,))
-        input_features["observation.state.subset2"] = PolicyFeature(type=FeatureType.STATE, shape=(5,))
-        input_features["observation.state"] = PolicyFeature(type=FeatureType.STATE, shape=(10,))
-
-        output_features = {}
-        output_features["action.first_three_motors"] = PolicyFeature(type=FeatureType.ACTION, shape=(3,))
-        output_features["action.last_two_motors"] = PolicyFeature(type=FeatureType.ACTION, shape=(2,))
-        output_features["action"] = PolicyFeature(
-            type=FeatureType.ACTION,
-            shape=(5,),
-        )
-
-    config = ACTConfig(input_features=input_features, output_features=output_features)
-
-    state_condition = config.robot_state_feature == input_features[OBS_STATE]
-    action_condition = config.action_feature == output_features[ACTION]
-
-    assert state_condition, (
-        f"Discrepancy detected. Robot state feature is {config.robot_state_feature} but policy expects {input_features[OBS_STATE]}"
-    )
-    assert action_condition, (
-        f"Discrepancy detected. Action feature is {config.action_feature} but policy expects {output_features[ACTION]}"
-    )
-
-
 @pytest.mark.parametrize(
     "ds_repo_id, policy_name, policy_kwargs, file_name_extra",
     [

tests/processor/test_batch_conversion.py

@@ -1,282 +0,0 @@
-import torch
-
-from lerobot.processor.pipeline import (
-    RobotProcessor,
-    TransitionKey,
-    _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[TransitionKey.OBSERVATION], dict)
-    assert "observation.image.top" in transition[TransitionKey.OBSERVATION]
-    assert "observation.image.left" in transition[TransitionKey.OBSERVATION]
-    assert "observation.state" in transition[TransitionKey.OBSERVATION]
-
-    # Check values are preserved
-    assert torch.allclose(
-        transition[TransitionKey.OBSERVATION]["observation.image.top"], batch["observation.image.top"]
-    )
-    assert torch.allclose(
-        transition[TransitionKey.OBSERVATION]["observation.image.left"], batch["observation.image.left"]
-    )
-    assert transition[TransitionKey.OBSERVATION]["observation.state"] == [1, 2, 3, 4]
-
-    # Check other fields
-    assert transition[TransitionKey.ACTION] == "action_data"
-    assert transition[TransitionKey.REWARD] == 1.5
-    assert transition[TransitionKey.DONE]
-    assert not transition[TransitionKey.TRUNCATED]
-    assert transition[TransitionKey.INFO] == {"episode": 42}
-    assert transition[TransitionKey.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 = {
-        TransitionKey.OBSERVATION: observation_dict,
-        TransitionKey.ACTION: "action_data",
-        TransitionKey.REWARD: 1.5,
-        TransitionKey.DONE: True,
-        TransitionKey.TRUNCATED: False,
-        TransitionKey.INFO: {"episode": 42},
-        TransitionKey.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[TransitionKey.OBSERVATION] is None
-
-    # Check other fields
-    assert transition[TransitionKey.ACTION] == "action_data"
-    assert transition[TransitionKey.REWARD] == 2.0
-    assert not transition[TransitionKey.DONE]
-    assert transition[TransitionKey.TRUNCATED]
-    assert transition[TransitionKey.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[TransitionKey.OBSERVATION] == {"observation.state": "minimal_state"}
-    assert transition[TransitionKey.ACTION] == "minimal_action"
-
-    # Check defaults
-    assert transition[TransitionKey.REWARD] == 0.0
-    assert not transition[TransitionKey.DONE]
-    assert not transition[TransitionKey.TRUNCATED]
-    assert transition[TransitionKey.INFO] == {}
-    assert transition[TransitionKey.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[TransitionKey.OBSERVATION] is None
-    assert transition[TransitionKey.ACTION] is None
-    assert transition[TransitionKey.REWARD] == 0.0
-    assert not transition[TransitionKey.DONE]
-    assert not transition[TransitionKey.TRUNCATED]
-    assert transition[TransitionKey.INFO] == {}
-    assert transition[TransitionKey.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 if k.startswith("observation.")}
-    reconstructed_obs_keys = {k for k in reconstructed_batch 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.get(TransitionKey.REWARD, 0.0)
-        new_tr = tr.copy()
-        new_tr[TransitionKey.REWARD] = reward * 2 if reward is not None else 0.0
-        return new_tr
-
-    def to_batch(tr):
-        batch = _default_transition_to_batch(tr)
-        return batch
-
-    processor = RobotProcessor(steps=[], to_transition=to_tr, to_output=to_batch)
-
-    batch = {
-        "observation.state": torch.randn(1, 4),
-        "action": torch.randn(1, 2),
-        "next.reward": 1.0,
-        "next.done": False,
-    }
-
-    result = processor(batch)
-
-    # Check the reward was doubled by our custom converter
-    assert result["next.reward"] == 2.0
-    assert torch.allclose(result["observation.state"], batch["observation.state"])
-    assert torch.allclose(result["action"], batch["action"])

tests/processor/test_normalize_processor.py

@@ -1,628 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from unittest.mock import Mock
-
-import numpy as np
-import pytest
-import torch
-
-from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
-from lerobot.processor.normalize_processor import (
-    NormalizerProcessor,
-    UnnormalizerProcessor,
-    _convert_stats_to_tensors,
-)
-from lerobot.processor.pipeline import RobotProcessor, TransitionKey
-
-
-def create_transition(
-    observation=None, action=None, reward=None, done=None, truncated=None, info=None, complementary_data=None
-):
-    """Helper to create an EnvTransition dictionary."""
-    return {
-        TransitionKey.OBSERVATION: observation,
-        TransitionKey.ACTION: action,
-        TransitionKey.REWARD: reward,
-        TransitionKey.DONE: done,
-        TransitionKey.TRUNCATED: truncated,
-        TransitionKey.INFO: info,
-        TransitionKey.COMPLEMENTARY_DATA: complementary_data,
-    }
-
-
-def test_numpy_conversion():
-    stats = {
-        "observation.image": {
-            "mean": np.array([0.5, 0.5, 0.5]),
-            "std": np.array([0.2, 0.2, 0.2]),
-        }
-    }
-    tensor_stats = _convert_stats_to_tensors(stats)
-
-    assert isinstance(tensor_stats["observation.image"]["mean"], torch.Tensor)
-    assert isinstance(tensor_stats["observation.image"]["std"], torch.Tensor)
-    assert torch.allclose(tensor_stats["observation.image"]["mean"], torch.tensor([0.5, 0.5, 0.5]))
-    assert torch.allclose(tensor_stats["observation.image"]["std"], torch.tensor([0.2, 0.2, 0.2]))
-
-
-def test_tensor_conversion():
-    stats = {
-        "action": {
-            "mean": torch.tensor([0.0, 0.0]),
-            "std": torch.tensor([1.0, 1.0]),
-        }
-    }
-    tensor_stats = _convert_stats_to_tensors(stats)
-
-    assert tensor_stats["action"]["mean"].dtype == torch.float32
-    assert tensor_stats["action"]["std"].dtype == torch.float32
-
-
-def test_scalar_conversion():
-    stats = {
-        "reward": {
-            "mean": 0.5,
-            "std": 0.1,
-        }
-    }
-    tensor_stats = _convert_stats_to_tensors(stats)
-
-    assert torch.allclose(tensor_stats["reward"]["mean"], torch.tensor(0.5))
-    assert torch.allclose(tensor_stats["reward"]["std"], torch.tensor(0.1))
-
-
-def test_list_conversion():
-    stats = {
-        "observation.state": {
-            "min": [0.0, -1.0, -2.0],
-            "max": [1.0, 1.0, 2.0],
-        }
-    }
-    tensor_stats = _convert_stats_to_tensors(stats)
-
-    assert torch.allclose(tensor_stats["observation.state"]["min"], torch.tensor([0.0, -1.0, -2.0]))
-    assert torch.allclose(tensor_stats["observation.state"]["max"], torch.tensor([1.0, 1.0, 2.0]))
-
-
-def test_unsupported_type():
-    stats = {
-        "bad_key": {
-            "mean": "string_value",
-        }
-    }
-    with pytest.raises(TypeError, match="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():
-    return {
-        "observation.image": {
-            "mean": np.array([0.5, 0.5, 0.5]),
-            "std": np.array([0.2, 0.2, 0.2]),
-        },
-        "observation.state": {
-            "min": np.array([0.0, -1.0]),
-            "max": np.array([1.0, 1.0]),
-        },
-    }
-
-
-@pytest.fixture
-def observation_normalizer(observation_stats):
-    """Return a NormalizerProcessor that only has observation stats (no action)."""
-    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):
-    observation = {
-        "observation.image": torch.tensor([0.7, 0.5, 0.3]),
-        "observation.state": torch.tensor([0.5, 0.0]),
-    }
-    transition = create_transition(observation=observation)
-
-    normalized_transition = observation_normalizer(transition)
-    normalized_obs = normalized_transition[TransitionKey.OBSERVATION]
-
-    # Check mean/std normalization
-    expected_image = (torch.tensor([0.7, 0.5, 0.3]) - 0.5) / 0.2
-    assert torch.allclose(normalized_obs["observation.image"], expected_image)
-
-
-def test_min_max_normalization(observation_normalizer):
-    observation = {
-        "observation.state": torch.tensor([0.5, 0.0]),
-    }
-    transition = create_transition(observation=observation)
-
-    normalized_transition = observation_normalizer(transition)
-    normalized_obs = normalized_transition[TransitionKey.OBSERVATION]
-
-    # Check min/max normalization to [-1, 1]
-    # For state[0]: 2 * (0.5 - 0.0) / (1.0 - 0.0) - 1 = 0.0
-    # For state[1]: 2 * (0.0 - (-1.0)) / (1.0 - (-1.0)) - 1 = 0.0
-    expected_state = torch.tensor([0.0, 0.0])
-    assert torch.allclose(normalized_obs["observation.state"], expected_state, atol=1e-6)
-
-
-def test_selective_normalization(observation_stats):
-    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]),
-        "observation.state": torch.tensor([0.5, 0.0]),
-    }
-    transition = create_transition(observation=observation)
-
-    normalized_transition = normalizer(transition)
-    normalized_obs = normalized_transition[TransitionKey.OBSERVATION]
-
-    # Only image should be normalized
-    assert torch.allclose(normalized_obs["observation.image"], (torch.tensor([0.7, 0.5, 0.3]) - 0.5) / 0.2)
-    # State should remain unchanged
-    assert torch.allclose(normalized_obs["observation.state"], observation["observation.state"])
-
-
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
-def test_device_compatibility(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(),
-    }
-    transition = create_transition(observation=observation)
-
-    normalized_transition = normalizer(transition)
-    normalized_obs = normalized_transition[TransitionKey.OBSERVATION]
-
-    assert normalized_obs["observation.image"].device.type == "cuda"
-
-
-def test_from_lerobot_dataset():
-    # Mock dataset
-    mock_dataset = Mock()
-    mock_dataset.meta.stats = {
-        "observation.image": {"mean": [0.5], "std": [0.2]},
-        "action": {"mean": [0.0], "std": [1.0]},
-    }
-
-    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
-    assert "action" in normalizer._tensor_stats
-
-
-def test_state_dict_save_load(observation_normalizer):
-    # Save state
-    state_dict = observation_normalizer.state_dict()
-
-    # Create new normalizer and load state
-    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
-    observation = {"observation.image": torch.tensor([0.7, 0.5, 0.3])}
-    transition = create_transition(observation=observation)
-
-    result1 = observation_normalizer(transition)[TransitionKey.OBSERVATION]
-    result2 = new_normalizer(transition)[TransitionKey.OBSERVATION]
-
-    assert torch.allclose(result1["observation.image"], result2["observation.image"])
-
-
-# Fixtures for ActionUnnormalizer tests
-@pytest.fixture
-def action_stats_mean_std():
-    return {
-        "mean": np.array([0.0, 0.0, 0.0]),
-        "std": np.array([1.0, 2.0, 0.5]),
-    }
-
-
-@pytest.fixture
-def action_stats_min_max():
-    return {
-        "min": np.array([-1.0, -2.0, 0.0]),
-        "max": np.array([1.0, 2.0, 1.0]),
-    }
-
-
-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):
-    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 = create_transition(action=normalized_action)
-
-    unnormalized_transition = unnormalizer(transition)
-    unnormalized_action = unnormalized_transition[TransitionKey.ACTION]
-
-    # action * std + mean
-    expected = torch.tensor([1.0 * 1.0 + 0.0, -0.5 * 2.0 + 0.0, 2.0 * 0.5 + 0.0])
-    assert torch.allclose(unnormalized_action, expected)
-
-
-def test_min_max_unnormalization(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])
-    transition = create_transition(action=normalized_action)
-
-    unnormalized_transition = unnormalizer(transition)
-    unnormalized_action = unnormalized_transition[TransitionKey.ACTION]
-
-    # Map from [-1, 1] to [min, max]
-    # (action + 1) / 2 * (max - min) + min
-    expected = torch.tensor(
-        [
-            (0.0 + 1) / 2 * (1.0 - (-1.0)) + (-1.0),  # 0.0
-            (-1.0 + 1) / 2 * (2.0 - (-2.0)) + (-2.0),  # -2.0
-            (1.0 + 1) / 2 * (1.0 - 0.0) + 0.0,  # 1.0
-        ]
-    )
-    assert torch.allclose(unnormalized_action, expected)
-
-
-def test_numpy_action_input(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 = create_transition(action=normalized_action)
-
-    unnormalized_transition = unnormalizer(transition)
-    unnormalized_action = unnormalized_transition[TransitionKey.ACTION]
-
-    assert isinstance(unnormalized_action, torch.Tensor)
-    expected = torch.tensor([1.0, -1.0, 1.0])
-    assert torch.allclose(unnormalized_action, expected)
-
-
-def test_none_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 = create_transition()
-    result = unnormalizer(transition)
-
-    # Should return transition unchanged
-    assert result == transition
-
-
-def test_action_from_lerobot_dataset():
-    mock_dataset = Mock()
-    mock_dataset.meta.stats = {"action": {"mean": [0.0], "std": [1.0]}}
-    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"]
-
-
-# Fixtures for NormalizerProcessor tests
-@pytest.fixture
-def full_stats():
-    return {
-        "observation.image": {
-            "mean": np.array([0.5, 0.5, 0.5]),
-            "std": np.array([0.2, 0.2, 0.2]),
-        },
-        "observation.state": {
-            "min": np.array([0.0, -1.0]),
-            "max": np.array([1.0, 1.0]),
-        },
-        "action": {
-            "mean": np.array([0.0, 0.0]),
-            "std": np.array([1.0, 2.0]),
-        },
-    }
-
-
-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):
-    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):
-    observation = {
-        "observation.image": torch.tensor([0.7, 0.5, 0.3]),
-        "observation.state": torch.tensor([0.5, 0.0]),
-    }
-    action = torch.tensor([1.0, -0.5])
-    transition = create_transition(
-        observation=observation,
-        action=action,
-        reward=1.0,
-        done=False,
-        truncated=False,
-        info={},
-        complementary_data={},
-    )
-
-    processed_transition = normalizer_processor(transition)
-
-    # Check normalized observations
-    processed_obs = processed_transition[TransitionKey.OBSERVATION]
-    expected_image = (torch.tensor([0.7, 0.5, 0.3]) - 0.5) / 0.2
-    assert torch.allclose(processed_obs["observation.image"], expected_image)
-
-    # Check normalized action
-    processed_action = processed_transition[TransitionKey.ACTION]
-    expected_action = torch.tensor([(1.0 - 0.0) / 1.0, (-0.5 - 0.0) / 2.0])
-    assert torch.allclose(processed_action, expected_action)
-
-    # Check other fields remain unchanged
-    assert processed_transition[TransitionKey.REWARD] == 1.0
-    assert not processed_transition[TransitionKey.DONE]
-
-
-def test_processor_from_lerobot_dataset(full_stats):
-    # Mock dataset
-    mock_dataset = Mock()
-    mock_dataset.meta.stats = full_stats
-
-    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
-    assert "action" in processor._tensor_stats
-
-
-def test_get_config(full_stats):
-    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()
-    expected_config = {
-        "normalize_keys": ["observation.image"],
-        "eps": 1e-6,
-        "features": {
-            "observation.image": {"type": "VISUAL", "shape": (3, 96, 96)},
-            "observation.state": {"type": "STATE", "shape": (2,)},
-            "action": {"type": "ACTION", "shape": (2,)},
-        },
-        "norm_map": {
-            "VISUAL": "MEAN_STD",
-            "STATE": "MIN_MAX",
-            "ACTION": "MEAN_STD",
-        },
-    }
-    assert config == expected_config
-
-
-def test_integration_with_robot_processor(normalizer_processor):
-    """Test integration with RobotProcessor pipeline"""
-    robot_processor = RobotProcessor([normalizer_processor])
-
-    observation = {
-        "observation.image": torch.tensor([0.7, 0.5, 0.3]),
-        "observation.state": torch.tensor([0.5, 0.0]),
-    }
-    action = torch.tensor([1.0, -0.5])
-    transition = create_transition(
-        observation=observation,
-        action=action,
-        reward=1.0,
-        done=False,
-        truncated=False,
-        info={},
-        complementary_data={},
-    )
-
-    processed_transition = robot_processor(transition)
-
-    # Verify the processing worked
-    assert isinstance(processed_transition[TransitionKey.OBSERVATION], dict)
-    assert isinstance(processed_transition[TransitionKey.ACTION], torch.Tensor)
-
-
-# Edge case tests
-def test_empty_observation():
-    stats = {"observation.image": {"mean": [0.5], "std": [0.2]}}
-    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 = create_transition()
-    result = normalizer(transition)
-
-    assert result == transition
-
-
-def test_empty_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 = create_transition(observation=observation)
-
-    result = normalizer(transition)
-    # Should return observation unchanged since no stats are available
-    assert torch.allclose(
-        result[TransitionKey.OBSERVATION]["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)
-    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 = create_transition(observation=observation)
-
-    processed = normalizer(transition)[TransitionKey.OBSERVATION]
-    assert torch.allclose(processed["observation.image"], observation["observation.image"])
-
-
-def test_missing_action_stats_no_error():
-    mock_dataset = Mock()
-    mock_dataset.meta.stats = {"observation.image": {"mean": [0.5], "std": [0.2]}}
-
-    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
-
-
-def test_serialization_roundtrip(full_stats):
-    """Test that features and norm_map can be serialized and deserialized correctly."""
-    features = _create_full_features()
-    norm_map = _create_full_norm_map()
-    original_processor = NormalizerProcessor(
-        features=features, norm_map=norm_map, stats=full_stats, normalize_keys={"observation.image"}, eps=1e-6
-    )
-
-    # Get config (serialization)
-    config = original_processor.get_config()
-
-    # Create a new processor from the config (deserialization)
-    new_processor = NormalizerProcessor(
-        features=config["features"],
-        norm_map=config["norm_map"],
-        stats=full_stats,
-        normalize_keys=set(config["normalize_keys"]),
-        eps=config["eps"],
-    )
-
-    # Test that both processors work the same way
-    observation = {
-        "observation.image": torch.tensor([0.7, 0.5, 0.3]),
-        "observation.state": torch.tensor([0.5, 0.0]),
-    }
-    action = torch.tensor([1.0, -0.5])
-    transition = create_transition(
-        observation=observation,
-        action=action,
-        reward=1.0,
-        done=False,
-        truncated=False,
-        info={},
-        complementary_data={},
-    )
-
-    result1 = original_processor(transition)
-    result2 = new_processor(transition)
-
-    # Compare results
-    assert torch.allclose(
-        result1[TransitionKey.OBSERVATION]["observation.image"],
-        result2[TransitionKey.OBSERVATION]["observation.image"],
-    )
-    assert torch.allclose(result1[TransitionKey.ACTION], result2[TransitionKey.ACTION])
-
-    # Verify features and norm_map are correctly reconstructed
-    assert new_processor.features.keys() == original_processor.features.keys()
-    for key in new_processor.features:
-        assert new_processor.features[key].type == original_processor.features[key].type
-        assert new_processor.features[key].shape == original_processor.features[key].shape
-
-    assert new_processor.norm_map == original_processor.norm_map

tests/processor/test_observation_processor.py

@@ -1,486 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import numpy as np
-import pytest
-import torch
-
-from lerobot.configs.types import FeatureType
-from lerobot.constants import OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE
-from lerobot.processor import VanillaObservationProcessor
-from lerobot.processor.pipeline import TransitionKey
-from tests.conftest import assert_contract_is_typed
-
-
-def create_transition(
-    observation=None, action=None, reward=None, done=None, truncated=None, info=None, complementary_data=None
-):
-    """Helper to create an EnvTransition dictionary."""
-    return {
-        TransitionKey.OBSERVATION: observation,
-        TransitionKey.ACTION: action,
-        TransitionKey.REWARD: reward,
-        TransitionKey.DONE: done,
-        TransitionKey.TRUNCATED: truncated,
-        TransitionKey.INFO: info,
-        TransitionKey.COMPLEMENTARY_DATA: complementary_data,
-    }
-
-
-def test_process_single_image():
-    """Test processing a single image."""
-    processor = VanillaObservationProcessor()
-
-    # Create a mock image (H, W, C) format, uint8
-    image = np.random.randint(0, 256, size=(64, 64, 3), dtype=np.uint8)
-
-    observation = {"pixels": image}
-    transition = create_transition(observation=observation)
-
-    result = processor(transition)
-    processed_obs = result[TransitionKey.OBSERVATION]
-
-    # Check that the image was processed correctly
-    assert "observation.image" in processed_obs
-    processed_img = processed_obs["observation.image"]
-
-    # Check shape: should be (1, 3, 64, 64) - batch, channels, height, width
-    assert processed_img.shape == (1, 3, 64, 64)
-
-    # Check dtype and range
-    assert processed_img.dtype == torch.float32
-    assert processed_img.min() >= 0.0
-    assert processed_img.max() <= 1.0
-
-
-def test_process_image_dict():
-    """Test processing multiple images in a dictionary."""
-    processor = VanillaObservationProcessor()
-
-    # Create mock images
-    image1 = np.random.randint(0, 256, size=(32, 32, 3), dtype=np.uint8)
-    image2 = np.random.randint(0, 256, size=(48, 48, 3), dtype=np.uint8)
-
-    observation = {"pixels": {"camera1": image1, "camera2": image2}}
-    transition = create_transition(observation=observation)
-
-    result = processor(transition)
-    processed_obs = result[TransitionKey.OBSERVATION]
-
-    # Check that both images were processed
-    assert "observation.images.camera1" in processed_obs
-    assert "observation.images.camera2" in processed_obs
-
-    # Check shapes
-    assert processed_obs["observation.images.camera1"].shape == (1, 3, 32, 32)
-    assert processed_obs["observation.images.camera2"].shape == (1, 3, 48, 48)
-
-
-def test_process_batched_image():
-    """Test processing already batched images."""
-    processor = VanillaObservationProcessor()
-
-    # Create a batched image (B, H, W, C)
-    image = np.random.randint(0, 256, size=(2, 64, 64, 3), dtype=np.uint8)
-
-    observation = {"pixels": image}
-    transition = create_transition(observation=observation)
-
-    result = processor(transition)
-    processed_obs = result[TransitionKey.OBSERVATION]
-
-    # Check that batch dimension is preserved
-    assert processed_obs["observation.image"].shape == (2, 3, 64, 64)
-
-
-def test_invalid_image_format():
-    """Test error handling for invalid image formats."""
-    processor = VanillaObservationProcessor()
-
-    # Test wrong channel order (channels first)
-    image = np.random.randint(0, 256, size=(3, 64, 64), dtype=np.uint8)
-    observation = {"pixels": image}
-    transition = create_transition(observation=observation)
-
-    with pytest.raises(ValueError, match="Expected channel-last images"):
-        processor(transition)
-
-
-def test_invalid_image_dtype():
-    """Test error handling for invalid image dtype."""
-    processor = VanillaObservationProcessor()
-
-    # Test wrong dtype
-    image = np.random.rand(64, 64, 3).astype(np.float32)
-    observation = {"pixels": image}
-    transition = create_transition(observation=observation)
-
-    with pytest.raises(ValueError, match="Expected torch.uint8 images"):
-        processor(transition)
-
-
-def test_no_pixels_in_observation():
-    """Test processor when no pixels are in observation."""
-    processor = VanillaObservationProcessor()
-
-    observation = {"other_data": np.array([1, 2, 3])}
-    transition = create_transition(observation=observation)
-
-    result = processor(transition)
-    processed_obs = result[TransitionKey.OBSERVATION]
-
-    # Should preserve other data unchanged
-    assert "other_data" in processed_obs
-    np.testing.assert_array_equal(processed_obs["other_data"], np.array([1, 2, 3]))
-
-
-def test_none_observation():
-    """Test processor with None observation."""
-    processor = VanillaObservationProcessor()
-
-    transition = create_transition()
-    result = processor(transition)
-
-    assert result == transition
-
-
-def test_serialization_methods():
-    """Test serialization methods."""
-    processor = VanillaObservationProcessor()
-
-    # Test get_config
-    config = processor.get_config()
-    assert isinstance(config, dict)
-
-    # Test state_dict
-    state = processor.state_dict()
-    assert isinstance(state, dict)
-
-    # Test load_state_dict (should not raise)
-    processor.load_state_dict(state)
-
-    # Test reset (should not raise)
-    processor.reset()
-
-
-def test_process_environment_state():
-    """Test processing environment_state."""
-    processor = VanillaObservationProcessor()
-
-    env_state = np.array([1.0, 2.0, 3.0], dtype=np.float32)
-    observation = {"environment_state": env_state}
-    transition = create_transition(observation=observation)
-
-    result = processor(transition)
-    processed_obs = result[TransitionKey.OBSERVATION]
-
-    # Check that environment_state was renamed and processed
-    assert "observation.environment_state" in processed_obs
-    assert "environment_state" not in processed_obs
-
-    processed_state = processed_obs["observation.environment_state"]
-    assert processed_state.shape == (1, 3)  # Batch dimension added
-    assert processed_state.dtype == torch.float32
-    torch.testing.assert_close(processed_state, torch.tensor([[1.0, 2.0, 3.0]]))
-
-
-def test_process_agent_pos():
-    """Test processing agent_pos."""
-    processor = VanillaObservationProcessor()
-
-    agent_pos = np.array([0.5, -0.5, 1.0], dtype=np.float32)
-    observation = {"agent_pos": agent_pos}
-    transition = create_transition(observation=observation)
-
-    result = processor(transition)
-    processed_obs = result[TransitionKey.OBSERVATION]
-
-    # Check that agent_pos was renamed and processed
-    assert "observation.state" in processed_obs
-    assert "agent_pos" not in processed_obs
-
-    processed_state = processed_obs["observation.state"]
-    assert processed_state.shape == (1, 3)  # Batch dimension added
-    assert processed_state.dtype == torch.float32
-    torch.testing.assert_close(processed_state, torch.tensor([[0.5, -0.5, 1.0]]))
-
-
-def test_process_batched_states():
-    """Test processing already batched states."""
-    processor = VanillaObservationProcessor()
-
-    env_state = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
-    agent_pos = np.array([[0.5, -0.5], [1.0, -1.0]], dtype=np.float32)
-
-    observation = {"environment_state": env_state, "agent_pos": agent_pos}
-    transition = create_transition(observation=observation)
-
-    result = processor(transition)
-    processed_obs = result[TransitionKey.OBSERVATION]
-
-    # Check that batch dimensions are preserved
-    assert processed_obs["observation.environment_state"].shape == (2, 2)
-    assert processed_obs["observation.state"].shape == (2, 2)
-
-
-def test_process_both_states():
-    """Test processing both environment_state and agent_pos."""
-    processor = VanillaObservationProcessor()
-
-    env_state = np.array([1.0, 2.0], dtype=np.float32)
-    agent_pos = np.array([0.5, -0.5], dtype=np.float32)
-
-    observation = {"environment_state": env_state, "agent_pos": agent_pos, "other_data": "keep_me"}
-    transition = create_transition(observation=observation)
-
-    result = processor(transition)
-    processed_obs = result[TransitionKey.OBSERVATION]
-
-    # Check that both states were processed
-    assert "observation.environment_state" in processed_obs
-    assert "observation.state" in processed_obs
-
-    # Check that original keys were removed
-    assert "environment_state" not in processed_obs
-    assert "agent_pos" not in processed_obs
-
-    # Check that other data was preserved
-    assert processed_obs["other_data"] == "keep_me"
-
-
-def test_no_states_in_observation():
-    """Test processor when no states are in observation."""
-    processor = VanillaObservationProcessor()
-
-    observation = {"other_data": np.array([1, 2, 3])}
-    transition = create_transition(observation=observation)
-
-    result = processor(transition)
-    processed_obs = result[TransitionKey.OBSERVATION]
-
-    # Should preserve data unchanged
-    np.testing.assert_array_equal(processed_obs, observation)
-
-
-def test_complete_observation_processing():
-    """Test processing a complete observation with both images and states."""
-    processor = VanillaObservationProcessor()
-
-    # Create mock data
-    image = np.random.randint(0, 256, size=(32, 32, 3), dtype=np.uint8)
-    env_state = np.array([1.0, 2.0, 3.0], dtype=np.float32)
-    agent_pos = np.array([0.5, -0.5, 1.0], dtype=np.float32)
-
-    observation = {
-        "pixels": image,
-        "environment_state": env_state,
-        "agent_pos": agent_pos,
-        "other_data": "preserve_me",
-    }
-    transition = create_transition(observation=observation)
-
-    result = processor(transition)
-    processed_obs = result[TransitionKey.OBSERVATION]
-
-    # Check that image was processed
-    assert "observation.image" in processed_obs
-    assert processed_obs["observation.image"].shape == (1, 3, 32, 32)
-
-    # Check that states were processed
-    assert "observation.environment_state" in processed_obs
-    assert "observation.state" in processed_obs
-
-    # Check that original keys were removed
-    assert "pixels" not in processed_obs
-    assert "environment_state" not in processed_obs
-    assert "agent_pos" not in processed_obs
-
-    # Check that other data was preserved
-    assert processed_obs["other_data"] == "preserve_me"
-
-
-def test_image_only_processing():
-    """Test processing observation with only images."""
-    processor = VanillaObservationProcessor()
-
-    image = np.random.randint(0, 256, size=(64, 64, 3), dtype=np.uint8)
-    observation = {"pixels": image}
-    transition = create_transition(observation=observation)
-
-    result = processor(transition)
-    processed_obs = result[TransitionKey.OBSERVATION]
-
-    assert "observation.image" in processed_obs
-    assert len(processed_obs) == 1
-
-
-def test_state_only_processing():
-    """Test processing observation with only states."""
-    processor = VanillaObservationProcessor()
-
-    agent_pos = np.array([1.0, 2.0], dtype=np.float32)
-    observation = {"agent_pos": agent_pos}
-    transition = create_transition(observation=observation)
-
-    result = processor(transition)
-    processed_obs = result[TransitionKey.OBSERVATION]
-
-    assert "observation.state" in processed_obs
-    assert "agent_pos" not in processed_obs
-
-
-def test_empty_observation():
-    """Test processing empty observation."""
-    processor = VanillaObservationProcessor()
-
-    observation = {}
-    transition = create_transition(observation=observation)
-
-    result = processor(transition)
-    processed_obs = result[TransitionKey.OBSERVATION]
-
-    assert processed_obs == {}
-
-
-def test_equivalent_to_original_function():
-    """Test that ObservationProcessor produces equivalent results to preprocess_observation."""
-    # Import the original function for comparison
-    from lerobot.envs.utils import preprocess_observation
-
-    processor = VanillaObservationProcessor()
-
-    # Create test data similar to what the original function expects
-    image = np.random.randint(0, 256, size=(64, 64, 3), dtype=np.uint8)
-    env_state = np.array([1.0, 2.0, 3.0], dtype=np.float32)
-    agent_pos = np.array([0.5, -0.5, 1.0], dtype=np.float32)
-
-    observation = {"pixels": image, "environment_state": env_state, "agent_pos": agent_pos}
-
-    # Process with original function
-    original_result = preprocess_observation(observation)
-
-    # Process with new processor
-    transition = create_transition(observation=observation)
-    processor_result = processor(transition)[TransitionKey.OBSERVATION]
-
-    # Compare results
-    assert set(original_result.keys()) == set(processor_result.keys())
-
-    for key in original_result:
-        torch.testing.assert_close(original_result[key], processor_result[key])
-
-
-def test_equivalent_with_image_dict():
-    """Test equivalence with dictionary of images."""
-    from lerobot.envs.utils import preprocess_observation
-
-    processor = VanillaObservationProcessor()
-
-    # Create test data with multiple cameras
-    image1 = np.random.randint(0, 256, size=(32, 32, 3), dtype=np.uint8)
-    image2 = np.random.randint(0, 256, size=(48, 48, 3), dtype=np.uint8)
-    agent_pos = np.array([1.0, 2.0], dtype=np.float32)
-
-    observation = {"pixels": {"cam1": image1, "cam2": image2}, "agent_pos": agent_pos}
-
-    # Process with original function
-    original_result = preprocess_observation(observation)
-
-    # Process with new processor
-    transition = create_transition(observation=observation)
-    processor_result = processor(transition)[TransitionKey.OBSERVATION]
-
-    # Compare results
-    assert set(original_result.keys()) == set(processor_result.keys())
-
-    for key in original_result:
-        torch.testing.assert_close(original_result[key], processor_result[key])
-
-
-def test_image_processor_feature_contract_pixels_to_image(policy_feature_factory):
-    processor = VanillaObservationProcessor()
-    features = {
-        "pixels": policy_feature_factory(FeatureType.VISUAL, (3, 64, 64)),
-        "keep": policy_feature_factory(FeatureType.ENV, (1,)),
-    }
-    out = processor.feature_contract(features.copy())
-
-    assert OBS_IMAGE in out and out[OBS_IMAGE] == features["pixels"]
-    assert "pixels" not in out
-    assert out["keep"] == features["keep"]
-    assert_contract_is_typed(out)
-
-
-def test_image_processor_feature_contract_observation_pixels_to_image(policy_feature_factory):
-    processor = VanillaObservationProcessor()
-    features = {
-        "observation.pixels": policy_feature_factory(FeatureType.VISUAL, (3, 64, 64)),
-        "keep": policy_feature_factory(FeatureType.ENV, (1,)),
-    }
-    out = processor.feature_contract(features.copy())
-
-    assert OBS_IMAGE in out and out[OBS_IMAGE] == features["observation.pixels"]
-    assert "observation.pixels" not in out
-    assert out["keep"] == features["keep"]
-    assert_contract_is_typed(out)
-
-
-def test_image_processor_feature_contract_multi_camera_and_prefixed(policy_feature_factory):
-    processor = VanillaObservationProcessor()
-    features = {
-        "pixels.front": policy_feature_factory(FeatureType.VISUAL, (3, 64, 64)),
-        "pixels.wrist": policy_feature_factory(FeatureType.VISUAL, (3, 64, 64)),
-        "observation.pixels.rear": policy_feature_factory(FeatureType.VISUAL, (3, 64, 64)),
-        "keep": policy_feature_factory(FeatureType.ENV, (7,)),
-    }
-    out = processor.feature_contract(features.copy())
-
-    assert f"{OBS_IMAGES}.front" in out and out[f"{OBS_IMAGES}.front"] == features["pixels.front"]
-    assert f"{OBS_IMAGES}.wrist" in out and out[f"{OBS_IMAGES}.wrist"] == features["pixels.wrist"]
-    assert f"{OBS_IMAGES}.rear" in out and out[f"{OBS_IMAGES}.rear"] == features["observation.pixels.rear"]
-    assert "pixels.front" not in out and "pixels.wrist" not in out and "observation.pixels.rear" not in out
-    assert out["keep"] == features["keep"]
-    assert_contract_is_typed(out)
-
-
-def test_state_processor_feature_contract_environment_and_agent_pos(policy_feature_factory):
-    processor = VanillaObservationProcessor()
-    features = {
-        "environment_state": policy_feature_factory(FeatureType.STATE, (3,)),
-        "agent_pos": policy_feature_factory(FeatureType.STATE, (7,)),
-        "keep": policy_feature_factory(FeatureType.ENV, (1,)),
-    }
-    out = processor.feature_contract(features.copy())
-
-    assert OBS_ENV_STATE in out and out[OBS_ENV_STATE] == features["environment_state"]
-    assert OBS_STATE in out and out[OBS_STATE] == features["agent_pos"]
-    assert "environment_state" not in out and "agent_pos" not in out
-    assert out["keep"] == features["keep"]
-    assert_contract_is_typed(out)
-
-
-def test_state_processor_feature_contract_prefixed_inputs(policy_feature_factory):
-    proc = VanillaObservationProcessor()
-    features = {
-        "observation.environment_state": policy_feature_factory(FeatureType.STATE, (2,)),
-        "observation.agent_pos": policy_feature_factory(FeatureType.STATE, (4,)),
-    }
-    out = proc.feature_contract(features.copy())
-
-    assert OBS_ENV_STATE in out and out[OBS_ENV_STATE] == features["observation.environment_state"]
-    assert OBS_STATE in out and out[OBS_STATE] == features["observation.agent_pos"]
-    assert "environment_state" not in out and "agent_pos" not in out
-    assert_contract_is_typed(out)

tests/processor/test_rename_processor.py

@@ -1,467 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import tempfile
-from pathlib import Path
-
-import numpy as np
-import torch
-
-from lerobot.configs.types import FeatureType
-from lerobot.processor import ProcessorStepRegistry, RenameProcessor, RobotProcessor, TransitionKey
-from tests.conftest import assert_contract_is_typed
-
-
-def create_transition(
-    observation=None, action=None, reward=None, done=None, truncated=None, info=None, complementary_data=None
-):
-    """Helper to create an EnvTransition dictionary."""
-    return {
-        TransitionKey.OBSERVATION: observation,
-        TransitionKey.ACTION: action,
-        TransitionKey.REWARD: reward,
-        TransitionKey.DONE: done,
-        TransitionKey.TRUNCATED: truncated,
-        TransitionKey.INFO: info,
-        TransitionKey.COMPLEMENTARY_DATA: complementary_data,
-    }
-
-
-def test_basic_renaming():
-    """Test basic key renaming functionality."""
-    rename_map = {
-        "old_key1": "new_key1",
-        "old_key2": "new_key2",
-    }
-    processor = RenameProcessor(rename_map=rename_map)
-
-    observation = {
-        "old_key1": torch.tensor([1.0, 2.0]),
-        "old_key2": np.array([3.0, 4.0]),
-        "unchanged_key": "keep_me",
-    }
-    transition = create_transition(observation=observation)
-
-    result = processor(transition)
-    processed_obs = result[TransitionKey.OBSERVATION]
-
-    # Check renamed keys
-    assert "new_key1" in processed_obs
-    assert "new_key2" in processed_obs
-    assert "old_key1" not in processed_obs
-    assert "old_key2" not in processed_obs
-
-    # Check values are preserved
-    torch.testing.assert_close(processed_obs["new_key1"], torch.tensor([1.0, 2.0]))
-    np.testing.assert_array_equal(processed_obs["new_key2"], np.array([3.0, 4.0]))
-
-    # Check unchanged key is preserved
-    assert processed_obs["unchanged_key"] == "keep_me"
-
-
-def test_empty_rename_map():
-    """Test processor with empty rename map (should pass through unchanged)."""
-    processor = RenameProcessor(rename_map={})
-
-    observation = {
-        "key1": torch.tensor([1.0]),
-        "key2": "value2",
-    }
-    transition = create_transition(observation=observation)
-
-    result = processor(transition)
-    processed_obs = result[TransitionKey.OBSERVATION]
-
-    # All keys should be unchanged
-    assert processed_obs.keys() == observation.keys()
-    torch.testing.assert_close(processed_obs["key1"], observation["key1"])
-    assert processed_obs["key2"] == observation["key2"]
-
-
-def test_none_observation():
-    """Test processor with None observation."""
-    processor = RenameProcessor(rename_map={"old": "new"})
-
-    transition = create_transition()
-    result = processor(transition)
-
-    # Should return transition unchanged
-    assert result == transition
-
-
-def test_overlapping_rename():
-    """Test renaming when new names might conflict."""
-    rename_map = {
-        "a": "b",
-        "b": "c",  # This creates a potential conflict
-    }
-    processor = RenameProcessor(rename_map=rename_map)
-
-    observation = {
-        "a": 1,
-        "b": 2,
-        "x": 3,
-    }
-    transition = create_transition(observation=observation)
-
-    result = processor(transition)
-    processed_obs = result[TransitionKey.OBSERVATION]
-
-    # Check that renaming happens correctly
-    assert "a" not in processed_obs
-    assert processed_obs["b"] == 1  # 'a' renamed to 'b'
-    assert processed_obs["c"] == 2  # original 'b' renamed to 'c'
-    assert processed_obs["x"] == 3
-
-
-def test_partial_rename():
-    """Test renaming only some keys."""
-    rename_map = {
-        "observation.state": "observation.proprio_state",
-        "pixels": "observation.image",
-    }
-    processor = RenameProcessor(rename_map=rename_map)
-
-    observation = {
-        "observation.state": torch.randn(10),
-        "pixels": np.random.randint(0, 256, (64, 64, 3), dtype=np.uint8),
-        "reward": 1.0,
-        "info": {"episode": 1},
-    }
-    transition = create_transition(observation=observation)
-
-    result = processor(transition)
-    processed_obs = result[TransitionKey.OBSERVATION]
-
-    # Check renamed keys
-    assert "observation.proprio_state" in processed_obs
-    assert "observation.image" in processed_obs
-    assert "observation.state" not in processed_obs
-    assert "pixels" not in processed_obs
-
-    # Check unchanged keys
-    assert processed_obs["reward"] == 1.0
-    assert processed_obs["info"] == {"episode": 1}
-
-
-def test_get_config():
-    """Test configuration serialization."""
-    rename_map = {
-        "old1": "new1",
-        "old2": "new2",
-    }
-    processor = RenameProcessor(rename_map=rename_map)
-
-    config = processor.get_config()
-    assert config == {"rename_map": rename_map}
-
-
-def test_state_dict():
-    """Test state dict (should be empty for RenameProcessor)."""
-    processor = RenameProcessor(rename_map={"old": "new"})
-
-    state = processor.state_dict()
-    assert state == {}
-
-    # Load state dict should work even with empty dict
-    processor.load_state_dict({})
-
-
-def test_integration_with_robot_processor():
-    """Test integration with RobotProcessor pipeline."""
-    rename_map = {
-        "agent_pos": "observation.state",
-        "pixels": "observation.image",
-    }
-    rename_processor = RenameProcessor(rename_map=rename_map)
-
-    pipeline = RobotProcessor([rename_processor])
-
-    observation = {
-        "agent_pos": np.array([1.0, 2.0, 3.0]),
-        "pixels": np.zeros((32, 32, 3), dtype=np.uint8),
-        "other_data": "preserve_me",
-    }
-    transition = create_transition(
-        observation=observation, reward=0.5, done=False, truncated=False, info={}, complementary_data={}
-    )
-
-    result = pipeline(transition)
-    processed_obs = result[TransitionKey.OBSERVATION]
-
-    # Check renaming worked through pipeline
-    assert "observation.state" in processed_obs
-    assert "observation.image" in processed_obs
-    assert "agent_pos" not in processed_obs
-    assert "pixels" not in processed_obs
-    assert processed_obs["other_data"] == "preserve_me"
-
-    # Check other transition elements unchanged
-    assert result[TransitionKey.REWARD] == 0.5
-    assert result[TransitionKey.DONE] is False
-
-
-def test_save_and_load_pretrained():
-    """Test saving and loading processor with RobotProcessor."""
-    rename_map = {
-        "old_state": "observation.state",
-        "old_image": "observation.image",
-    }
-    processor = RenameProcessor(rename_map=rename_map)
-    pipeline = RobotProcessor([processor], name="TestRenameProcessor")
-
-    with tempfile.TemporaryDirectory() as tmp_dir:
-        # Save pipeline
-        pipeline.save_pretrained(tmp_dir)
-
-        # Check files were created
-        config_path = Path(tmp_dir) / "testrenameprocessor.json"  # Based on name="TestRenameProcessor"
-        assert config_path.exists()
-
-        # No state files should be created for RenameProcessor
-        state_files = list(Path(tmp_dir).glob("*.safetensors"))
-        assert len(state_files) == 0
-
-        # Load pipeline
-        loaded_pipeline = RobotProcessor.from_pretrained(tmp_dir)
-
-        assert loaded_pipeline.name == "TestRenameProcessor"
-        assert len(loaded_pipeline) == 1
-
-        # Check that loaded processor works correctly
-        loaded_processor = loaded_pipeline.steps[0]
-        assert isinstance(loaded_processor, RenameProcessor)
-        assert loaded_processor.rename_map == rename_map
-
-        # Test functionality after loading
-        observation = {"old_state": [1, 2, 3], "old_image": "image_data"}
-        transition = create_transition(observation=observation)
-
-        result = loaded_pipeline(transition)
-        processed_obs = result[TransitionKey.OBSERVATION]
-
-        assert "observation.state" in processed_obs
-        assert "observation.image" in processed_obs
-        assert processed_obs["observation.state"] == [1, 2, 3]
-        assert processed_obs["observation.image"] == "image_data"
-
-
-def test_registry_functionality():
-    """Test that RenameProcessor is properly registered."""
-    # Check that it's registered
-    assert "rename_processor" in ProcessorStepRegistry.list()
-
-    # Get from registry
-    retrieved_class = ProcessorStepRegistry.get("rename_processor")
-    assert retrieved_class is RenameProcessor
-
-    # Create instance from registry
-    instance = retrieved_class(rename_map={"old": "new"})
-    assert isinstance(instance, RenameProcessor)
-    assert instance.rename_map == {"old": "new"}
-
-
-def test_registry_based_save_load():
-    """Test save/load using registry name instead of module path."""
-    processor = RenameProcessor(rename_map={"key1": "renamed_key1"})
-    pipeline = RobotProcessor([processor])
-
-    with tempfile.TemporaryDirectory() as tmp_dir:
-        # Save and load
-        pipeline.save_pretrained(tmp_dir)
-
-        # Verify config uses registry name
-        import json
-
-        with open(Path(tmp_dir) / "robotprocessor.json") as f:  # Default name is "RobotProcessor"
-            config = json.load(f)
-
-        assert "registry_name" in config["steps"][0]
-        assert config["steps"][0]["registry_name"] == "rename_processor"
-        assert "class" not in config["steps"][0]  # Should use registry, not module path
-
-        # Load should work
-        loaded_pipeline = RobotProcessor.from_pretrained(tmp_dir)
-        loaded_processor = loaded_pipeline.steps[0]
-        assert isinstance(loaded_processor, RenameProcessor)
-        assert loaded_processor.rename_map == {"key1": "renamed_key1"}
-
-
-def test_chained_rename_processors():
-    """Test multiple RenameProcessors in a pipeline."""
-    # First processor: rename raw keys to intermediate format
-    processor1 = RenameProcessor(
-        rename_map={
-            "pos": "agent_position",
-            "img": "camera_image",
-        }
-    )
-
-    # Second processor: rename to final format
-    processor2 = RenameProcessor(
-        rename_map={
-            "agent_position": "observation.state",
-            "camera_image": "observation.image",
-        }
-    )
-
-    pipeline = RobotProcessor([processor1, processor2])
-
-    observation = {
-        "pos": np.array([1.0, 2.0]),
-        "img": "image_data",
-        "extra": "keep_me",
-    }
-    transition = create_transition(observation=observation)
-
-    # Step through to see intermediate results
-    results = list(pipeline.step_through(transition))
-
-    # After first processor
-    assert "agent_position" in results[1][TransitionKey.OBSERVATION]
-    assert "camera_image" in results[1][TransitionKey.OBSERVATION]
-
-    # After second processor
-    final_obs = results[2][TransitionKey.OBSERVATION]
-    assert "observation.state" in final_obs
-    assert "observation.image" in final_obs
-    assert final_obs["extra"] == "keep_me"
-
-    # Original keys should be gone
-    assert "pos" not in final_obs
-    assert "img" not in final_obs
-    assert "agent_position" not in final_obs
-    assert "camera_image" not in final_obs
-
-
-def test_nested_observation_rename():
-    """Test renaming with nested observation structures."""
-    rename_map = {
-        "observation.images.left": "observation.camera.left_view",
-        "observation.images.right": "observation.camera.right_view",
-        "observation.proprio": "observation.proprioception",
-    }
-    processor = RenameProcessor(rename_map=rename_map)
-
-    observation = {
-        "observation.images.left": torch.randn(3, 64, 64),
-        "observation.images.right": torch.randn(3, 64, 64),
-        "observation.proprio": torch.randn(7),
-        "observation.gripper": torch.tensor([0.0]),  # Not renamed
-    }
-    transition = create_transition(observation=observation)
-
-    result = processor(transition)
-    processed_obs = result[TransitionKey.OBSERVATION]
-
-    # Check renames
-    assert "observation.camera.left_view" in processed_obs
-    assert "observation.camera.right_view" in processed_obs
-    assert "observation.proprioception" in processed_obs
-
-    # Check unchanged key
-    assert "observation.gripper" in processed_obs
-
-    # Check old keys removed
-    assert "observation.images.left" not in processed_obs
-    assert "observation.images.right" not in processed_obs
-    assert "observation.proprio" not in processed_obs
-
-
-def test_value_types_preserved():
-    """Test that various value types are preserved during renaming."""
-    rename_map = {"old_tensor": "new_tensor", "old_array": "new_array", "old_scalar": "new_scalar"}
-    processor = RenameProcessor(rename_map=rename_map)
-
-    tensor_value = torch.randn(3, 3)
-    array_value = np.random.rand(2, 2)
-
-    observation = {
-        "old_tensor": tensor_value,
-        "old_array": array_value,
-        "old_scalar": 42,
-        "old_string": "hello",
-        "old_dict": {"nested": "value"},
-        "old_list": [1, 2, 3],
-    }
-    transition = create_transition(observation=observation)
-
-    result = processor(transition)
-    processed_obs = result[TransitionKey.OBSERVATION]
-
-    # Check that values and types are preserved
-    assert torch.equal(processed_obs["new_tensor"], tensor_value)
-    assert np.array_equal(processed_obs["new_array"], array_value)
-    assert processed_obs["new_scalar"] == 42
-    assert processed_obs["old_string"] == "hello"
-    assert processed_obs["old_dict"] == {"nested": "value"}
-    assert processed_obs["old_list"] == [1, 2, 3]
-
-
-def test_feature_contract_basic_renaming(policy_feature_factory):
-    processor = RenameProcessor(rename_map={"a": "x", "b": "y"})
-    features = {
-        "a": policy_feature_factory(FeatureType.STATE, (2,)),
-        "b": policy_feature_factory(FeatureType.ACTION, (3,)),
-        "c": policy_feature_factory(FeatureType.ENV, (1,)),
-    }
-
-    out = processor.feature_contract(features.copy())
-
-    # Values preserved and typed
-    assert out["x"] == features["a"]
-    assert out["y"] == features["b"]
-    assert out["c"] == features["c"]
-
-    assert_contract_is_typed(out)
-    # Input not mutated
-    assert set(features) == {"a", "b", "c"}
-
-
-def test_feature_contract_overlapping_keys(policy_feature_factory):
-    # Overlapping renames: both 'a' and 'b' exist. 'a'->'b', 'b'->'c'
-    processor = RenameProcessor(rename_map={"a": "b", "b": "c"})
-    features = {
-        "a": policy_feature_factory(FeatureType.STATE, (1,)),
-        "b": policy_feature_factory(FeatureType.STATE, (2,)),
-    }
-    out = processor.feature_contract(features)
-
-    assert set(out) == {"b", "c"}
-    assert out["b"] == features["a"]  # 'a' renamed to'b'
-    assert out["c"] == features["b"]  # 'b' renamed to 'c'
-    assert_contract_is_typed(out)
-
-
-def test_feature_contract_chained_processors(policy_feature_factory):
-    # Chain two rename processors at the contract level
-    processor1 = RenameProcessor(rename_map={"pos": "agent_position", "img": "camera_image"})
-    processor2 = RenameProcessor(
-        rename_map={"agent_position": "observation.state", "camera_image": "observation.image"}
-    )
-    pipeline = RobotProcessor([processor1, processor2])
-
-    spec = {
-        "pos": policy_feature_factory(FeatureType.STATE, (7,)),
-        "img": policy_feature_factory(FeatureType.VISUAL, (3, 64, 64)),
-        "extra": policy_feature_factory(FeatureType.ENV, (1,)),
-    }
-    out = pipeline.feature_contract(initial_features=spec)
-
-    assert set(out) == {"observation.state", "observation.image", "extra"}
-    assert out["observation.state"] == spec["pos"]
-    assert out["observation.image"] == spec["img"]
-    assert out["extra"] == spec["extra"]
-    assert_contract_is_typed(out)