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Add RobotProcessor tutorial to documentation
- Introduced a new tutorial on using RobotProcessor for preprocessing robot data. - Added a section in the table of contents for easy navigation to the new tutorial. - The tutorial covers key concepts, real-world scenarios, and practical examples for effective use of the RobotProcessor pipeline.
This commit is contained in:
Adil Zouitine
@@ -13,6 +13,8 @@
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title: Cameras
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- local: integrate_hardware
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title: Bring Your Own Hardware
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- local: processor_tutorial
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title: RobotProcessor Pipeline
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- local: hilserl
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title: Train a Robot with RL
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- local: hilserl_sim
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@@ -0,0 +1,847 @@
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# RobotProcessor: The Art of Processing Robot Data
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This tutorial will teach you how to use RobotProcessor, a powerful pipeline system for preprocessing robot observations and postprocessing actions. You'll learn how to create modular, composable data transformations that make your robot learning pipelines more maintainable and reproducible.
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**You'll learn:**
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1. What RobotProcessor is and why it's better than alternatives
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2. How to create and compose processor steps
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3. How to save, load, and share your processors
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4. Advanced features for debugging and customization
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## Real-World Scenarios: When You Need RobotProcessor
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Before diving into code, let's understand the real problems RobotProcessor solves. If you've worked with robots and learned policies, you've likely encountered these challenges:
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### Observation Key Mismatches
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Your environment might output observations with keys like `"rgb_camera_front"` and `"joint_positions"`, but your pre-trained policy expects `"observation.images.wrist"` and `"observation.state"`. You need to rename these keys consistently across your pipeline.
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### Image Preprocessing Requirements
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Your robot's camera captures 1920x1080 RGB images, but your policy was trained on 224x224 cropped images focusing on the workspace. You need to:
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- Crop out the relevant workspace area
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- Resize to the expected dimensions
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- Convert from uint8 [0,255] to float32 [0,1]
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- Ensure the right channel ordering (HWC vs CHW)
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- Move everything to GPU efficiently
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### Coordinate System Transformations
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Your policy outputs desired end-effector positions in Cartesian space, but your robot expects joint angles. You need to:
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- Apply inverse kinematics to convert positions to joint angles
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- Handle singularities and out-of-reach positions
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- Smooth the resulting trajectories to avoid jerky movements
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- Clip values to respect joint limits
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### State Augmentation
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Your environment provides joint positions, but your policy also needs velocities and accelerations. You need to:
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- Calculate velocities from position differences
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- Maintain a buffer of past states
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- Handle episode boundaries correctly
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### Multi-Robot Deployment
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You want to deploy the same policy on different robots with varying:
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- Camera setups (different resolutions, positions)
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- Joint configurations (6-DOF vs 7-DOF arms)
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- Control frequencies (10Hz vs 30Hz)
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- Safety limits and workspace boundaries
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### Data Collection and Training
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During data collection, you need to:
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- Record raw sensor data for dataset creation
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- Apply the same preprocessing as during deployment
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- Ensure reproducibility across different operators
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- Share preprocessing configurations with collaborators
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RobotProcessor solves all these problems by providing a modular, composable system where each transformation is a separate, shareable component. Let's see how.
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## Why RobotProcessor?
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If you've worked with robot learning before, you've likely written preprocessing code like this:
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```python
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# Traditional procedural approach - hard to maintain and share
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def preprocess_observation(obs, device='cuda'):
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processed_obs = {}
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# Process images
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if "pixels" in obs:
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for cam_name, img in obs["pixels"].items():
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# Convert uint8 [0, 255] to float32 [0, 1]
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img = img.astype(np.float32) / 255.0
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# Convert from HWC to CHW format
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img = np.transpose(img, (2, 0, 1))
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# Add batch dimension
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img = np.expand_dims(img, axis=0)
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# Convert to torch and move to device
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img = torch.from_numpy(img).to(device)
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# Pad to standard size
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if img.shape[-2:] != (224, 224):
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img = F.pad(img, (0, 224 - img.shape[-1], 0, 224 - img.shape[-2]))
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processed_obs[f"observation.images.{cam_name}"] = img
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# Process state
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if "agent_pos" in obs:
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state = torch.from_numpy(obs["agent_pos"]).float()
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state = state.unsqueeze(0).to(device)
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processed_obs["observation.state"] = state
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return processed_obs
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```
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This approach has several problems:
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- **Mixed concerns**: Image processing, tensor conversion, device management all in one function
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- **Hard to test**: Can't test image processing separately from state processing
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- **Device management scattered**: Easy to forget `.to(device)` calls
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- **No standardized format**: Each project reinvents the wheel
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- **Difficult to share**: Can't easily reuse this preprocessing in other projects
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RobotProcessor solves these issues by providing a declarative pipeline approach where each transformation is a separate, testable, shareable component.
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## Understanding EnvTransition
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Before diving into RobotProcessor, let's understand the data structure it operates on. An `EnvTransition` is a 7-tuple that represents a complete transition in the environment:
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```python
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from lerobot.processor.pipeline import TransitionIndex
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# EnvTransition structure:
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# (observation, action, reward, done, truncated, info, complementary_data)
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transition = (
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{"pixels": ..., "agent_pos": ...}, # observation at time t
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[0.1, -0.2, 0.3], # action taken at time t
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1.0, # reward received
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False, # episode done flag
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False, # episode truncated flag
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{"success": True}, # additional info from environment
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{"step_idx": 42} # complementary_data for inter-step communication
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)
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```
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Each element serves a specific purpose:
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1. **observation**: Raw sensor data from the environment (images, states, etc.)
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2. **action**: The action command sent to the robot
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3. **reward**: Scalar reward signal (for RL tasks)
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4. **done**: Boolean indicating natural episode termination
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5. **truncated**: Boolean indicating artificial termination (time limit, safety)
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6. **info**: Dictionary with environment-specific information
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7. **complementary_data**: Dictionary for passing data between processor steps (NEW!)
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### Using TransitionIndex
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Instead of using magic numbers to access tuple elements, use the `TransitionIndex` enum:
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```python
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from lerobot.processor.pipeline import TransitionIndex
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# Bad - using magic numbers
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obs = transition[0]
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action = transition[1]
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# Good - using TransitionIndex
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obs = transition[TransitionIndex.OBSERVATION]
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action = transition[TransitionIndex.ACTION]
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reward = transition[TransitionIndex.REWARD]
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done = transition[TransitionIndex.DONE]
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truncated = transition[TransitionIndex.TRUNCATED]
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info = transition[TransitionIndex.INFO]
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comp_data = transition[TransitionIndex.COMPLEMENTARY_DATA]
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```
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## Your First RobotProcessor
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Let's create a processor that properly handles image and state preprocessing:
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```python
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from lerobot.processor.pipeline import RobotProcessor, TransitionIndex
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from lerobot.processor.observation_processor import ImageProcessor, StateProcessor
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import numpy as np
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# Create sample data
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observation = {
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"pixels": {
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"camera_front": np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8),
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"camera_side": np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)
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},
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"agent_pos": np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7], dtype=np.float32)
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}
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# Create a full transition
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transition = (observation, None, 0.0, False, False, {}, {})
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# Create and use the processor
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processor = RobotProcessor([
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ImageProcessor(), # Converts uint8[0,255] to float32[0,1], HWC to CHW, adds batch dim
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StateProcessor(), # Converts numpy arrays to torch tensors, adds batch dim
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])
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processed_transition = processor(transition)
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processed_obs = processed_transition[TransitionIndex.OBSERVATION]
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# Check the results
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print("Original keys:", observation.keys())
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print("Processed keys:", processed_obs.keys())
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print("Image shape:", processed_obs["observation.images.camera_front"].shape) # [1, 3, 480, 640]
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print("Image dtype:", processed_obs["observation.images.camera_front"].dtype) # torch.float32
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print("Image range:", processed_obs["observation.images.camera_front"].min().item(),
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"to", processed_obs["observation.images.camera_front"].max().item()) # 0.0 to 1.0
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```
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## Creating Custom Steps: The ProcessorStep Protocol
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A processor step must follow certain conventions. Let's create a complete example that shows all required and optional methods:
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```python
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from dataclasses import dataclass
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from typing import Any, Dict
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import torch
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import torch.nn.functional as F
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@dataclass
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class ImagePadder:
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"""Pad images to a standard size for batch processing."""
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target_height: int = 224
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target_width: int = 224
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pad_value: float = 0.0
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def __call__(self, transition: EnvTransition) -> EnvTransition:
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"""Main processing method - required for all steps."""
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obs = transition[TransitionIndex.OBSERVATION]
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if obs is None:
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return transition
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# Process all image observations
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for key in list(obs.keys()):
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if key.startswith("observation.images."):
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img = obs[key]
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# Calculate padding
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_, _, h, w = img.shape
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pad_h = max(0, self.target_height - h)
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pad_w = max(0, self.target_width - w)
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if pad_h > 0 or pad_w > 0:
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# Pad symmetrically
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pad_left = pad_w // 2
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pad_right = pad_w - pad_left
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pad_top = pad_h // 2
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pad_bottom = pad_h - pad_top
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img = F.pad(img, (pad_left, pad_right, pad_top, pad_bottom),
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mode='constant', value=self.pad_value)
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obs[key] = img
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# Return modified transition
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return (obs, *transition[1:])
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def get_config(self) -> Dict[str, Any]:
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"""Return JSON-serializable configuration - required for save/load."""
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return {
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"target_height": self.target_height,
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"target_width": self.target_width,
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"pad_value": self.pad_value
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}
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def state_dict(self) -> Dict[str, torch.Tensor]:
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"""Return tensor state - only include torch.Tensor objects!"""
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# This step has no learnable parameters
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return {}
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def load_state_dict(self, state: Dict[str, torch.Tensor]) -> None:
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"""Load tensor state - required if state_dict returns non-empty dict."""
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# Nothing to load for this step
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pass
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def reset(self) -> None:
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"""Reset internal state at episode boundaries - required for stateful steps."""
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# This step is stateless, so nothing to reset
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pass
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```
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### Important: get_config vs state_dict
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These two methods serve different purposes and it's crucial to use them correctly:
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```python
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@dataclass
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class AdaptiveNormalizer:
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"""Example showing proper use of get_config and state_dict."""
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learning_rate: float = 0.01
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epsilon: float = 1e-8
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def __post_init__(self):
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self.running_mean = None
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self.running_var = None
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self.num_samples = 0 # Python int, not tensor
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def get_config(self) -> Dict[str, Any]:
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"""ONLY Python objects that can be JSON serialized!"""
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return {
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"learning_rate": self.learning_rate, # float ✓
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"epsilon": self.epsilon, # float ✓
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"num_samples": self.num_samples, # int ✓
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# "running_mean": self.running_mean, # torch.Tensor ✗ WRONG!
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}
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def state_dict(self) -> Dict[str, torch.Tensor]:
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"""ONLY torch.Tensor objects!"""
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if self.running_mean is None:
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return {}
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return {
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"running_mean": self.running_mean, # torch.Tensor ✓
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"running_var": self.running_var, # torch.Tensor ✓
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# "num_samples": self.num_samples, # int ✗ WRONG!
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# Instead, convert to tensor if needed:
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"num_samples_tensor": torch.tensor(self.num_samples)
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}
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def load_state_dict(self, state: Dict[str, torch.Tensor]) -> None:
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"""Load tensors and convert back to Python types if needed."""
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self.running_mean = state.get("running_mean")
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self.running_var = state.get("running_var")
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if "num_samples_tensor" in state:
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self.num_samples = int(state["num_samples_tensor"].item())
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```
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## Using Complementary Data for Inter-Step Communication
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The `complementary_data` field is perfect for passing information between steps without polluting the info dictionary:
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```python
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@dataclass
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class ImageStatisticsCalculator:
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"""Calculate image statistics and pass to next steps."""
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def __call__(self, transition: EnvTransition) -> EnvTransition:
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obs = transition[TransitionIndex.OBSERVATION]
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comp_data = transition[TransitionIndex.COMPLEMENTARY_DATA] or {}
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if obs is None:
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return transition
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# Calculate statistics for all images
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image_stats = {}
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for key in obs:
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if key.startswith("observation.images."):
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img = obs[key]
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stats = {
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"mean": img.mean().item(),
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"std": img.std().item(),
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"min": img.min().item(),
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"max": img.max().item(),
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}
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image_stats[key] = stats
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# Store in complementary_data for next steps
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comp_data["image_statistics"] = image_stats
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# Return transition with updated complementary_data
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return (
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obs,
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transition[TransitionIndex.ACTION],
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transition[TransitionIndex.REWARD],
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transition[TransitionIndex.DONE],
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transition[TransitionIndex.TRUNCATED],
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transition[TransitionIndex.INFO],
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comp_data # Updated complementary_data
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)
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@dataclass
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class AdaptiveBrightnessAdjuster:
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"""Adjust brightness based on statistics from previous step."""
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target_brightness: float = 0.5
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def __call__(self, transition: EnvTransition) -> EnvTransition:
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obs = transition[TransitionIndex.OBSERVATION]
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comp_data = transition[TransitionIndex.COMPLEMENTARY_DATA] or {}
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if obs is None or "image_statistics" not in comp_data:
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return transition
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# Use statistics from previous step
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image_stats = comp_data["image_statistics"]
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for key in obs:
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if key.startswith("observation.images.") and key in image_stats:
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current_mean = image_stats[key]["mean"]
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brightness_adjust = self.target_brightness - current_mean
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# Adjust brightness
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obs[key] = torch.clamp(obs[key] + brightness_adjust, 0, 1)
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return (obs, *transition[1:])
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# Use them together
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processor = RobotProcessor([
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ImageProcessor(),
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ImageStatisticsCalculator(), # Calculates stats
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AdaptiveBrightnessAdjuster(), # Uses stats from previous step
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])
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```
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## Complete Example: Device-Aware Processing Pipeline
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Here's a complete example showing proper device management and all features:
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```python
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from lerobot.processor.pipeline import RobotProcessor, ProcessorStepRegistry, TransitionIndex
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import torch
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import torch.nn.functional as F
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import numpy as np
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@ProcessorStepRegistry.register("device_mover")
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@dataclass
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class DeviceMover:
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"""Move all tensors to specified device."""
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device: str = "cuda"
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def __call__(self, transition: EnvTransition) -> EnvTransition:
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obs = transition[TransitionIndex.OBSERVATION]
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if obs is None:
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return transition
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# Move all tensor observations to device
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for key, value in obs.items():
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if isinstance(value, torch.Tensor):
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obs[key] = value.to(self.device)
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# Also handle action if present
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action = transition[TransitionIndex.ACTION]
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if action is not None and isinstance(action, torch.Tensor):
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action = action.to(self.device)
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return (obs, action, *transition[2:])
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return (obs, *transition[1:])
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def get_config(self) -> Dict[str, Any]:
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return {"device": str(self.device)}
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@ProcessorStepRegistry.register("running_normalizer")
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@dataclass
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class RunningNormalizer:
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"""Normalize using running statistics with proper device handling."""
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feature_dim: int
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momentum: float = 0.1
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def __post_init__(self):
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# Initialize as None - will be created on first call with correct device
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self.running_mean = None
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self.running_var = None
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self.initialized = False
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def __call__(self, transition: EnvTransition) -> EnvTransition:
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obs = transition[TransitionIndex.OBSERVATION]
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if obs is None or "observation.state" not in obs:
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return transition
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state = obs["observation.state"]
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# Initialize on first call with correct device
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if not self.initialized:
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device = state.device
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self.running_mean = torch.zeros(self.feature_dim, device=device)
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self.running_var = torch.ones(self.feature_dim, device=device)
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self.initialized = True
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# Update statistics
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with torch.no_grad():
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batch_mean = state.mean(dim=0)
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batch_var = state.var(dim=0, unbiased=False)
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self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * batch_mean
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self.running_var = (1 - self.momentum) * self.running_var + self.momentum * batch_var
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# Normalize
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state_normalized = (state - self.running_mean) / (self.running_var + 1e-8).sqrt()
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obs["observation.state"] = state_normalized
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return (obs, *transition[1:])
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def get_config(self) -> Dict[str, Any]:
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return {
|
||||
"feature_dim": self.feature_dim,
|
||||
"momentum": self.momentum,
|
||||
"initialized": self.initialized
|
||||
}
|
||||
|
||||
def state_dict(self) -> Dict[str, torch.Tensor]:
|
||||
if not self.initialized:
|
||||
return {}
|
||||
return {
|
||||
"running_mean": self.running_mean,
|
||||
"running_var": self.running_var
|
||||
}
|
||||
|
||||
def load_state_dict(self, state: Dict[str, torch.Tensor]) -> None:
|
||||
if state:
|
||||
self.running_mean = state["running_mean"]
|
||||
self.running_var = state["running_var"]
|
||||
self.initialized = True
|
||||
|
||||
def reset(self) -> None:
|
||||
# Don't reset statistics - they persist across episodes
|
||||
pass
|
||||
|
||||
# Create a complete pipeline
|
||||
processor = RobotProcessor([
|
||||
ImageProcessor(), # Convert images to float32 [0,1]
|
||||
StateProcessor(), # Convert states to torch tensors
|
||||
ImagePadder(224, 224), # Pad images to standard size
|
||||
DeviceMover("cuda"), # Move everything to GPU
|
||||
RunningNormalizer(7), # Normalize states
|
||||
], name="CompletePreprocessor")
|
||||
|
||||
# The processor handles device transfers automatically
|
||||
processor = processor.to("cuda") # Moves all stateful components to GPU
|
||||
|
||||
# Use it
|
||||
obs = {
|
||||
"pixels": {"cam": np.random.randint(0, 255, (200, 300, 3), dtype=np.uint8)},
|
||||
"agent_pos": np.random.randn(7).astype(np.float32)
|
||||
}
|
||||
transition = (obs, None, 0.0, False, False, {}, {})
|
||||
|
||||
# Everything is processed and on GPU
|
||||
processed = processor(transition)
|
||||
print(processed[TransitionIndex.OBSERVATION]["observation.images.cam"].device) # cuda:0
|
||||
```
|
||||
|
||||
## Solving Real-World Problems with RobotProcessor
|
||||
|
||||
Let's see how RobotProcessor elegantly solves the scenarios we discussed earlier:
|
||||
|
||||
### Renaming Observation Keys
|
||||
|
||||
When your environment and policy speak different "languages":
|
||||
|
||||
```python
|
||||
@ProcessorStepRegistry.register("key_remapper")
|
||||
@dataclass
|
||||
class KeyRemapper:
|
||||
"""Rename observation keys to match policy expectations."""
|
||||
|
||||
key_mapping: Dict[str, str] = field(default_factory=lambda: {
|
||||
"rgb_camera_front": "observation.images.wrist",
|
||||
"joint_positions": "observation.state",
|
||||
"gripper_state": "observation.gripper"
|
||||
})
|
||||
|
||||
def __call__(self, transition: EnvTransition) -> EnvTransition:
|
||||
obs = transition[TransitionIndex.OBSERVATION]
|
||||
if obs is None:
|
||||
return transition
|
||||
|
||||
# Create new observation with renamed keys
|
||||
renamed_obs = {}
|
||||
for old_key, new_key in self.key_mapping.items():
|
||||
if old_key in obs:
|
||||
renamed_obs[new_key] = obs[old_key]
|
||||
|
||||
# Keep any unmapped keys as-is
|
||||
for key, value in obs.items():
|
||||
if key not in self.key_mapping:
|
||||
renamed_obs[key] = value
|
||||
|
||||
return (renamed_obs, *transition[1:])
|
||||
```
|
||||
|
||||
### Workspace-Focused Image Processing
|
||||
|
||||
When you need to crop and resize images to focus on the manipulation workspace:
|
||||
|
||||
```python
|
||||
@ProcessorStepRegistry.register("workspace_cropper")
|
||||
@dataclass
|
||||
class WorkspaceCropper:
|
||||
"""Crop and resize images to focus on robot workspace."""
|
||||
|
||||
crop_bbox: Tuple[int, int, int, int] = (400, 200, 1200, 800) # (x1, y1, x2, y2)
|
||||
output_size: Tuple[int, int] = (224, 224)
|
||||
|
||||
def __call__(self, transition: EnvTransition) -> EnvTransition:
|
||||
obs = transition[TransitionIndex.OBSERVATION]
|
||||
if obs is None:
|
||||
return transition
|
||||
|
||||
for key in list(obs.keys()):
|
||||
if key.startswith("observation.images."):
|
||||
img = obs[key]
|
||||
# Crop to workspace
|
||||
x1, y1, x2, y2 = self.crop_bbox
|
||||
img_cropped = img[:, :, y1:y2, x1:x2]
|
||||
# Resize to expected dimensions
|
||||
img_resized = F.interpolate(
|
||||
img_cropped,
|
||||
size=self.output_size,
|
||||
mode='bilinear',
|
||||
align_corners=False
|
||||
)
|
||||
obs[key] = img_resized
|
||||
|
||||
return (obs, *transition[1:])
|
||||
```
|
||||
|
||||
|
||||
### Building Complete Pipelines for Different Robots
|
||||
|
||||
Now you can compose these steps into robot-specific pipelines:
|
||||
|
||||
```python
|
||||
# Pipeline for Robot A with wrist camera
|
||||
robot_a_processor = RobotProcessor([
|
||||
# Observation preprocessing
|
||||
KeyRemapper({
|
||||
"wrist_rgb": "observation.images.wrist",
|
||||
"arm_joints": "observation.state"
|
||||
}),
|
||||
ImageProcessor(),
|
||||
WorkspaceCropper(crop_bbox=(300, 100, 900, 700), output_size=(224, 224)),
|
||||
StateProcessor(),
|
||||
VelocityCalculator(state_key="observation.state"),
|
||||
DeviceMover("cuda"),
|
||||
|
||||
# Action postprocessing
|
||||
ActionSmoother(alpha=0.2),
|
||||
], name="RobotA_ACT_Processor")
|
||||
|
||||
# Pipeline for Robot B with overhead camera
|
||||
robot_b_processor = RobotProcessor([
|
||||
# Different key mapping for Robot B
|
||||
KeyRemapper({
|
||||
"overhead_cam": "observation.images.wrist", # Reuse same policy!
|
||||
"joint_encoders": "observation.state"
|
||||
}),
|
||||
ImageProcessor(),
|
||||
WorkspaceCropper(crop_bbox=(100, 50, 1100, 950), output_size=(224, 224)),
|
||||
StateProcessor(),
|
||||
VelocityCalculator(state_key="observation.state"),
|
||||
DeviceMover("cuda"),
|
||||
|
||||
ActionSmoother(alpha=0.3), # Different smoothing
|
||||
], name="RobotB_ACT_Processor")
|
||||
|
||||
# Share processors with the community
|
||||
robot_a_processor.push_to_hub("my-lab/robot-a-act-processor")
|
||||
robot_b_processor.push_to_hub("my-lab/robot-b-act-processor")
|
||||
|
||||
# Now anyone can use the same preprocessing
|
||||
processor = RobotProcessor.from_pretrained("my-lab/robot-a-act-processor")
|
||||
```
|
||||
|
||||
The beauty of this approach is that:
|
||||
1. **Each transformation is tested independently** - The IK solver can be validated separately from image cropping
|
||||
2. **Configurations are shareable** - Other labs can use your exact preprocessing setup
|
||||
3. **Pipelines are composable** - Mix and match steps for different robots
|
||||
4. **Everything is version controlled** - Use Hub revisions to track changes
|
||||
|
||||
## Best Practices for Processor Steps
|
||||
|
||||
### 1. Always Check for None
|
||||
```python
|
||||
def __call__(self, transition: EnvTransition) -> EnvTransition:
|
||||
obs = transition[TransitionIndex.OBSERVATION]
|
||||
|
||||
# Always check if observation exists
|
||||
if obs is None:
|
||||
return transition
|
||||
|
||||
# Also check for specific keys
|
||||
if "observation.state" not in obs:
|
||||
return transition
|
||||
```
|
||||
|
||||
### 2. Preserve Transition Structure
|
||||
```python
|
||||
# Good - preserve all elements
|
||||
return (modified_obs, *transition[1:])
|
||||
|
||||
# Bad - losing information
|
||||
return (modified_obs, None, 0.0, False, False, {}, {})
|
||||
```
|
||||
|
||||
### 3. Clone When Storing State
|
||||
```python
|
||||
def __call__(self, transition: EnvTransition) -> EnvTransition:
|
||||
obs = transition[TransitionIndex.OBSERVATION]
|
||||
|
||||
if self.store_previous:
|
||||
# Good - clone to avoid reference issues
|
||||
self.previous_state = obs["observation.state"].clone()
|
||||
|
||||
# Bad - stores reference that might be modified
|
||||
# self.previous_state = obs["observation.state"]
|
||||
```
|
||||
|
||||
### 4. Handle Device Transfers in state_dict
|
||||
```python
|
||||
def state_dict(self) -> Dict[str, torch.Tensor]:
|
||||
if self.buffer is None:
|
||||
return {}
|
||||
|
||||
# Good - clone to avoid memory sharing issues
|
||||
return {"buffer": self.buffer.clone()}
|
||||
```
|
||||
|
||||
## Complete Policy Example with Pre and Post Processing
|
||||
|
||||
Here's how to use RobotProcessor in a real robot control loop:
|
||||
|
||||
```python
|
||||
from lerobot.processor.pipeline import RobotProcessor, ProcessorStepRegistry, TransitionIndex
|
||||
from lerobot.policies.act.modeling_act import ACTPolicy
|
||||
from pathlib import Path
|
||||
import time
|
||||
|
||||
# Create preprocessing pipeline
|
||||
preprocessor = RobotProcessor([
|
||||
ImageProcessor(),
|
||||
StateProcessor(),
|
||||
ImagePadder(target_height=224, target_width=224),
|
||||
DeviceMover("cuda"),
|
||||
RunningNormalizer(feature_dim=14), # For 14-DOF robot
|
||||
], name="ACTPreprocessor")
|
||||
|
||||
# Create postprocessing pipeline for actions
|
||||
@ProcessorStepRegistry.register("action_clipper")
|
||||
@dataclass
|
||||
class ActionClipper:
|
||||
"""Clip actions to safe ranges."""
|
||||
min_value: float = -1.0
|
||||
max_value: float = 1.0
|
||||
|
||||
def __call__(self, transition: EnvTransition) -> EnvTransition:
|
||||
action = transition[TransitionIndex.ACTION]
|
||||
|
||||
if action is not None:
|
||||
action = torch.clamp(action, self.min_value, self.max_value)
|
||||
return (transition[TransitionIndex.OBSERVATION], action, *transition[2:])
|
||||
|
||||
return transition
|
||||
|
||||
def get_config(self) -> Dict[str, Any]:
|
||||
return {"min_value": self.min_value, "max_value": self.max_value}
|
||||
|
||||
postprocessor = RobotProcessor([
|
||||
ActionClipper(min_value=-0.5, max_value=0.5),
|
||||
ActionSmoother(alpha=0.3),
|
||||
], name="ACTPostprocessor")
|
||||
|
||||
# Load policy
|
||||
policy = ACTPolicy.from_pretrained("lerobot/act_aloha_sim_transfer_cube_human")
|
||||
|
||||
# Move everything to GPU
|
||||
preprocessor = preprocessor.to("cuda")
|
||||
postprocessor = postprocessor.to("cuda")
|
||||
policy = policy.to("cuda")
|
||||
|
||||
# Control loop
|
||||
env = make_robot_env()
|
||||
obs, info = env.reset()
|
||||
|
||||
for episode in range(10):
|
||||
print(f"Episode {episode + 1}")
|
||||
|
||||
for step in range(1000):
|
||||
# Create transition with raw observation
|
||||
transition = (obs, None, 0.0, False, False, info, {"step": step})
|
||||
|
||||
# Preprocess
|
||||
processed_transition = preprocessor(transition)
|
||||
processed_obs = processed_transition[TransitionIndex.OBSERVATION]
|
||||
|
||||
# Get action from policy
|
||||
with torch.no_grad():
|
||||
action = policy.select_action(processed_obs)
|
||||
|
||||
# Postprocess action
|
||||
action_transition = (
|
||||
processed_obs,
|
||||
action,
|
||||
0.0,
|
||||
False,
|
||||
False,
|
||||
info,
|
||||
{"raw_action": action.clone()} # Store raw action in complementary_data
|
||||
)
|
||||
processed_action_transition = postprocessor(action_transition)
|
||||
final_action = processed_action_transition[TransitionIndex.ACTION]
|
||||
|
||||
# Execute action
|
||||
obs, reward, terminated, truncated, info = env.step(final_action.cpu().numpy())
|
||||
|
||||
if terminated or truncated:
|
||||
# Reset at episode boundary
|
||||
preprocessor.reset()
|
||||
postprocessor.reset()
|
||||
obs, info = env.reset()
|
||||
break
|
||||
|
||||
# Save preprocessor with learned statistics
|
||||
preprocessor.save_pretrained(f"./checkpoints/preprocessor_ep{episode}")
|
||||
|
||||
# Push final version to hub
|
||||
preprocessor.push_to_hub("my-username/act-preprocessor")
|
||||
postprocessor.push_to_hub("my-username/act-postprocessor")
|
||||
```
|
||||
|
||||
## Debugging and Monitoring
|
||||
|
||||
Use the full power of `RobotProcessor` for debugging:
|
||||
|
||||
```python
|
||||
# Enable detailed logging
|
||||
def log_observation_shapes(step_idx: int, transition: EnvTransition):
|
||||
obs = transition[TransitionIndex.OBSERVATION]
|
||||
if obs:
|
||||
print(f"Step {step_idx} observations:")
|
||||
for key, value in obs.items():
|
||||
if isinstance(value, torch.Tensor):
|
||||
print(f" {key}: shape={value.shape}, device={value.device}, dtype={value.dtype}")
|
||||
return None
|
||||
|
||||
processor.register_after_step_hook(log_observation_shapes)
|
||||
|
||||
# Monitor complementary data flow
|
||||
def monitor_complementary_data(step_idx: int, transition: EnvTransition):
|
||||
comp_data = transition[TransitionIndex.COMPLEMENTARY_DATA]
|
||||
if comp_data:
|
||||
print(f"Step {step_idx} complementary_data: {list(comp_data.keys())}")
|
||||
return None
|
||||
|
||||
processor.register_before_step_hook(monitor_complementary_data)
|
||||
|
||||
# Validate data integrity
|
||||
def validate_tensors(step_idx: int, transition: EnvTransition):
|
||||
obs = transition[TransitionIndex.OBSERVATION]
|
||||
if obs:
|
||||
for key, value in obs.items():
|
||||
if isinstance(value, torch.Tensor):
|
||||
if torch.isnan(value).any():
|
||||
raise ValueError(f"NaN detected in {key} after step {step_idx}")
|
||||
if torch.isinf(value).any():
|
||||
raise ValueError(f"Inf detected in {key} after step {step_idx}")
|
||||
return None
|
||||
|
||||
processor.register_after_step_hook(validate_tensors)
|
||||
```
|
||||
|
||||
## Summary
|
||||
|
||||
RobotProcessor provides a powerful, modular approach to data preprocessing in robotics:
|
||||
|
||||
- **Clear separation of concerns**: Each transformation is a separate, testable unit
|
||||
- **Proper state management**: Clear distinction between config (JSON) and state (tensors)
|
||||
- **Device-aware**: Seamless GPU/CPU transfers with `.to(device)`
|
||||
- **Inter-step communication**: Use `complementary_data` for passing information
|
||||
- **Easy sharing**: Push to Hugging Face Hub for reproducibility
|
||||
- **Type safety**: Use `TransitionIndex` instead of magic numbers
|
||||
- **Debugging tools**: Step through transformations and add monitoring hooks
|
||||
|
||||
By following these patterns, your preprocessing code becomes more maintainable, shareable, and robust.
|
||||
|
||||
For the full API reference, see the [RobotProcessor API documentation](/api/processor).
|
||||
Reference in New Issue
Block a user