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LeRobot’s processor pipeline system transforms raw robot data through multiple steps. This guide shows you how to debug and inspect data at each stage of the pipeline.

​
Understanding the Pipeline

A processor pipeline is a sequence of ProcessorStep objects that transform data: 
from lerobot.processor import DataProcessorPipeline

pipeline = DataProcessorPipeline(
    steps=[
        DeviceProcessorStep(device="cuda"),
        NormalizerProcessorStep(stats=dataset_stats),
        AddBatchDimensionProcessorStep(),
    ],
    name="my_pipeline"
)

# Process data through full pipeline
output = pipeline(input_data)
See lerobot/processor/pipeline.py:254 for the full DataProcessorPipeline implementation.

​
Step-Through Debugging

Use step_through() to inspect data after each processing step: 
from lerobot.processor import create_transition

# Create sample data
transition = create_transition(
    observation={"camera": image, "state": joint_positions},
    action=action_tensor,
)

# Step through pipeline
for i, intermediate_transition in enumerate(pipeline.step_through(transition)):
    print(f"\nAfter step {i}:")
    
    if i == 0:
        print("  Initial state")
    else:
        step = pipeline.steps[i - 1]
        print(f"  Step: {step.__class__.__name__}")
    
    # Inspect observation
    obs = intermediate_transition.get('observation')
    if obs:
        for key, value in obs.items():
            if isinstance(value, torch.Tensor):
                print(f"  {key}: shape={value.shape}, dtype={value.dtype}, device={value.device}")
    
    # Inspect action
    action = intermediate_transition.get('action')
    if isinstance(action, torch.Tensor):
        print(f"  action: shape={action.shape}, dtype={action.dtype}, device={action.device}")
Example output: 
After step 0:
  Initial state
  camera: shape=(480, 640, 3), dtype=torch.uint8, device=cpu
  state: shape=(7,), dtype=torch.float32, device=cpu
  action: shape=(7,), dtype=torch.float32, device=cpu

After step 1:
  Step: DeviceProcessorStep
  camera: shape=(480, 640, 3), dtype=torch.uint8, device=cuda:0
  state: shape=(7,), dtype=torch.float32, device=cuda:0
  action: shape=(7,), dtype=torch.float32, device=cuda:0

After step 2:
  Step: NormalizerProcessorStep
  camera: shape=(480, 640, 3), dtype=torch.float32, device=cuda:0  # Normalized to [0,1]
  state: shape=(7,), dtype=torch.float32, device=cuda:0  # Normalized by stats
  action: shape=(7,), dtype=torch.float32, device=cuda:0  # Normalized by stats

​
Custom Hooks

Add hooks to inspect or modify data at specific pipeline stages: 
def before_step_hook(step_idx: int, transition: dict):
    """Called before each step"""
    step_name = pipeline.steps[step_idx].__class__.__name__
    print(f"\n→ Before {step_name}")
    
    # Check for NaN values
    for key, value in transition.items():
        if isinstance(value, dict):
            for k, v in value.items():
                if isinstance(v, torch.Tensor) and torch.isnan(v).any():
                    print(f"  ⚠️ NaN detected in {key}.{k}")

def after_step_hook(step_idx: int, transition: dict):
    """Called after each step"""
    step_name = pipeline.steps[step_idx].__class__.__name__
    print(f"← After {step_name}")
    
    # Log statistics
    action = transition.get('action')
    if isinstance(action, torch.Tensor):
        print(f"  Action stats: mean={action.mean():.4f}, std={action.std():.4f}")

# Create pipeline with hooks
pipeline = DataProcessorPipeline(
    steps=[...],
    before_step_hooks=[before_step_hook],
    after_step_hooks=[after_step_hook]
)

​
Inspecting Feature Transformations

Track how data shapes and types change through the pipeline: 
from lerobot.configs.types import FeatureType, PolicyFeature, PipelineFeatureType

# Define input features
input_features = {
    PipelineFeatureType.OBSERVATION: {
        "observation.images.camera": PolicyFeature(type=FeatureType.IMAGE, shape=(3, 224, 224)),
        "observation.state": PolicyFeature(type=FeatureType.STATE, shape=(7,)),
    },
    PipelineFeatureType.ACTION: {
        "action": PolicyFeature(type=FeatureType.ACTION, shape=(7,)),
    },
}

# Transform through each step
current_features = input_features
for i, step in enumerate(pipeline.steps):
    print(f"\nStep {i}: {step.__class__.__name__}")
    
    # Get transformed features
    current_features = step.transform_features(current_features)
    
    # Print feature descriptions
    for feature_type, features in current_features.items():
        print(f"  {feature_type}:")
        for name, feature in features.items():
            print(f"    {name}: shape={feature.shape}, type={feature.type}")
Example output: 
Step 0: TokenizerProcessorStep
  OBSERVATION:
    observation.language_tokens: shape=(77,), type=TEXT
    observation.language_attention_mask: shape=(77,), type=TEXT
    observation.images.camera: shape=(3, 224, 224), type=IMAGE
    observation.state: shape=(7,), type=STATE
  ACTION:
    action: shape=(7,), type=ACTION

Step 1: NormalizerProcessorStep
  OBSERVATION:
    observation.language_tokens: shape=(77,), type=TEXT
    observation.language_attention_mask: shape=(77,), type=TEXT
    observation.images.camera: shape=(3, 224, 224), type=IMAGE
    observation.state: shape=(7,), type=STATE  # Normalized
  ACTION:
    action: shape=(7,), type=ACTION  # Normalized

​
Debugging Normalization

Inspect normalization statistics: 
from lerobot.processor import NormalizerProcessorStep

normalizer = None
for step in pipeline.steps:
    if isinstance(step, NormalizerProcessorStep):
        normalizer = step
        break

if normalizer:
    print("Normalization stats:")
    for key, stats in normalizer.stats.items():
        print(f"\n{key}:")
        if 'mean' in stats:
            print(f"  mean: {stats['mean']}")
            print(f"  std: {stats['std']}")
        if 'min' in stats:
            print(f"  min: {stats['min']}")
            print(f"  max: {stats['max']}")
    
    # Check tensor stats (on device)
    print("\nTensor stats:")
    for key, tensor_stats in normalizer._tensor_stats.items():
        print(f"\n{key}:")
        for stat_name, tensor in tensor_stats.items():
            print(f"  {stat_name}: device={tensor.device}, dtype={tensor.dtype}")

​
Debugging Device Placement

Verify tensors are on correct devices: 
def check_device_placement(transition: dict, expected_device: str = "cuda"):
    """Recursively check all tensors are on expected device"""
    issues = []
    
    def check_recursive(obj, path=""):
        if isinstance(obj, torch.Tensor):
            if obj.device.type != expected_device:
                issues.append(f"{path}: expected {expected_device}, got {obj.device}")
        elif isinstance(obj, dict):
            for key, value in obj.items():
                check_recursive(value, f"{path}.{key}" if path else key)
    
    check_recursive(transition)
    
    if issues:
        print("❌ Device placement issues:")
        for issue in issues:
            print(f"  {issue}")
    else:
        print(f"✅ All tensors on {expected_device}")

# Use in pipeline
for i, transition in enumerate(pipeline.step_through(data)):
    if i > 0 and isinstance(pipeline.steps[i-1], DeviceProcessorStep):
        check_device_placement(transition, pipeline.steps[i-1].device)

​
Saving Pipeline State

Save pipeline configuration and state for debugging: 
from pathlib import Path

# Save pipeline
pipeline.save_pretrained("debug_pipeline")

# Check saved files
save_dir = Path("debug_pipeline")
print("Saved files:")
for file in save_dir.iterdir():
    print(f"  {file.name}")
    if file.suffix == ".json":
        import json
        with open(file) as f:
            config = json.load(f)
        print(json.dumps(config, indent=2))

​
Common Issues and Solutions

​
Issue: NaN Values After Normalization

Cause: Division by zero when std is 0 Solution: Check normalization stats: 
for key, stats in normalizer.stats.items():
    if 'std' in stats:
        std = torch.tensor(stats['std'])
        if (std == 0).any():
            print(f"⚠️ Zero std in {key}: {std}")
            print("   This will cause NaN values!")

​
Issue: Device Mismatch Errors

Cause: Tensors on different devices Solution: Add device processor early in pipeline: 
pipeline = DataProcessorPipeline(
    steps=[
        DeviceProcessorStep(device="cuda"),  # First step!
        # ... other steps
    ]
)

​
Issue: Shape Mismatches

Cause: Step expecting different input shape Solution: Use transform_features() to verify: 
current_features = input_features
for step in pipeline.steps:
    print(f"Before {step.__class__.__name__}:")
    print(f"  Features: {current_features}")
    
    try:
        current_features = step.transform_features(current_features)
    except Exception as e:
        print(f"❌ Error in {step.__class__.__name__}: {e}")
        break

​
Debugging Custom Processors

Add debug output to custom processor steps: 
from lerobot.processor import ProcessorStep, ProcessorStepRegistry
from dataclasses import dataclass

@ProcessorStepRegistry.register("debug_processor")
@dataclass
class DebugProcessorStep(ProcessorStep):
    verbose: bool = True
    
    def __call__(self, transition):
        if self.verbose:
            print(f"\n=== {self.__class__.__name__} ===")
            print(f"Input keys: {transition.keys()}")
            
            for key, value in transition.items():
                if isinstance(value, torch.Tensor):
                    print(f"{key}: {value.shape}, {value.dtype}, {value.device}")
                elif isinstance(value, dict):
                    print(f"{key}: dict with {len(value)} items")
                    for k, v in value.items():
                        if isinstance(v, torch.Tensor):
                            print(f"  {k}: {v.shape}, {v.dtype}, {v.device}")
        
        return transition
    
    def transform_features(self, features):
        return features  # Pass through

# Use in pipeline
pipeline = DataProcessorPipeline(
    steps=[
        DebugProcessorStep(verbose=True),
        DeviceProcessorStep(device="cuda"),
        DebugProcessorStep(verbose=True),
        NormalizerProcessorStep(stats=stats),
        DebugProcessorStep(verbose=True),
    ]
)

​
Performance Profiling

Measure time spent in each step: 
import time

timings = []

def timing_hook(step_idx: int, transition: dict):
    if not hasattr(timing_hook, 'start_time'):
        timing_hook.start_time = time.perf_counter()
    else:
        elapsed = time.perf_counter() - timing_hook.start_time
        step_name = pipeline.steps[step_idx].__class__.__name__
        timings.append((step_name, elapsed))
        timing_hook.start_time = time.perf_counter()

pipeline = DataProcessorPipeline(
    steps=[...],
    after_step_hooks=[timing_hook]
)

# Run pipeline
output = pipeline(data)

# Print timings
print("\nPipeline timings:")
total = sum(t for _, t in timings)
for step_name, elapsed in timings:
    pct = 100 * elapsed / total
    print(f"{step_name:30s}: {elapsed*1000:6.2f}ms ({pct:5.1f}%)")
print(f"{'Total':30s}: {total*1000:6.2f}ms")

​
API Reference

​
DataProcessorPipeline

See lerobot/processor/pipeline.py:254
​
step_through
(data) -> Iterable[EnvTransition]
Yield intermediate states after each processing step
​
__call__
(data) -> Any
Process data through full pipeline
​
save_pretrained
(path) -> None
Save pipeline configuration and state

​
ProcessorStep

See lerobot/processor/pipeline.py:143
​
transform_features
(features) -> dict
Transform feature descriptions without processing actual data
​
state_dict
() -> dict
Return processor state (e.g., normalization stats)
​
load_state_dict
(state) -> None
Load processor state from dictionary

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