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docs/source/introduction_processors.mdx

@@ -3,16 +3,19 @@
 In robotics, there's a fundamental mismatch between the data that robots and humans produce and what machine learning models expect. This creates several translation challenges:
 
 **Raw Robot Data → Model Input:**
+
 - Robots output raw sensor data (camera images, joint positions, force readings) that need normalization, batching, and device placement before models can process them
 - Language instructions from humans ("pick up the red cube") must be tokenized into numerical representations
 - Different robots use different coordinate systems and units that need standardization
 
 **Model Output → Robot Commands:**
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 - Models might output end-effector positions, but robots need joint-space commands
 - Teleoperators (like gamepads) produce relative movements (delta positions), but robots expect absolute commands
 - Model predictions are often normalized and need to be converted back to real-world scales
 
 **Cross-Domain Translation:**
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 - Training data from one robot setup needs adaptation for deployment on different hardware
 - Models trained with specific camera configurations must work with new camera arrangements
 - Datasets with different naming conventions need harmonization
@@ -24,6 +27,7 @@ Processors are the data transformation backbone of LeRobot. They handle all the
 ## What are Processors?
 
 In robotics, data comes in many forms - images from cameras, joint positions from sensors, text instructions from users, and more. Each type of data requires specific transformations before a model can use it effectively. Models need this data to be:
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 - **Normalized**: Scaled to appropriate ranges for neural network processing
 - **Batched**: Organized with proper dimensions for batch processing
 - **Tokenized**: Text converted to numerical representations
@@ -63,6 +67,7 @@ transition: EnvTransition = {
 ```
 
 Each key in the transition has a specific purpose:
+
 - **OBSERVATION**: All sensor data (images, states, proprioception)
 - **ACTION**: The action to execute or that was executed
 - **REWARD**: Reinforcement learning signal
@@ -146,7 +151,6 @@ output = processor(transition)  # Stays as EnvTransition throughout
 The `to_transition` and `to_output` converters enable seamless integration with existing codebases.
 By default, they handle the standard LeRobot batch format, but you can customize them for different data structures.
 
-
 ### Data Format Conversion
 
 Different data sources have different formats, but processors need a unified `EnvTransition` structure internally.
@@ -351,6 +355,7 @@ Different datasets and models may use different naming conventions.
 The `RenameProcessor` solves this mismatch:
 
 **Why is this useful?**
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 - When loading a model trained on a different dataset with different key names
 - When using foundation models that expect specific key naming conventions
 - When standardizing datasets from different sources
@@ -818,6 +823,7 @@ variant_processor = RobotProcessor(
 ### 1. Order Matters
 
 The sequence of processors is crucial. Follow this general order:
+
 ```python
 # Preprocessing: Raw → Model-ready
 1. Rename (standardize keys)
@@ -851,6 +857,7 @@ print(ProcessorStepRegistry.list())  # See all registered processors
 ### 3. Common Pitfalls and Solutions
 
 **Tensor Device Mismatch:**
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 ```python
 # Problem: RuntimeError: Expected all tensors on same device
 # Solution: Ensure DeviceProcessor is in pipeline
@@ -863,6 +870,7 @@ preprocessor = RobotProcessor(
 ```
 
 **Missing Statistics:**
+
 ```python
 # Problem: NormalizerProcessor has no stats
 # Solution 1: Compute stats from dataset