Migrate policy to PolicyProcessorPipeline system

🤖 **Automated Policy Migration to PolicyProcessorPipeline**

This PR migrates your model to the new LeRobot policy format using the modern PolicyProcessorPipeline architecture.

## What Changed

### ✨ **New Architecture - PolicyProcessorPipeline**
Your model now uses external PolicyProcessorPipeline components for data processing instead of built-in normalization layers. This provides:
- **Modularity**: Separate preprocessing and postprocessing pipelines
- **Flexibility**: Easy to swap, configure, and debug processing steps
- **Compatibility**: Works with the latest LeRobot ecosystem

### 🔧 **Normalization Extraction**
We've extracted normalization statistics from your model's state_dict and removed the built-in normalization layers:
- **Extracted patterns**: `normalize_inputs.*`, `unnormalize_outputs.*`, `normalize.*`, `unnormalize.*`, `input_normalizer.*`, `output_normalizer.*`
- **Statistics preserved**: Mean, std, min, max values for all features
- **Clean model**: State dict now contains only core model weights

### 📦 **Files Added**
- **preprocessor_config.json**: Configuration for input preprocessing pipeline
- **postprocessor_config.json**: Configuration for output postprocessing pipeline
- **model.safetensors**: Clean model weights without normalization layers
- **config.json**: Updated model configuration
- **train_config.json**: Training configuration
- **README.md**: Updated model card with migration information

### 🚀 **Benefits**
- **Backward Compatible**: Your model behavior remains identical
- **Future Ready**: Compatible with latest LeRobot features and updates
- **Debuggable**: Easy to inspect and modify processing steps
- **Portable**: Processors can be shared and reused across models

### 💻 **Usage**
```python
# Load your migrated model
from lerobot.policies import get_policy_class
from lerobot.processor import PolicyProcessorPipeline

# The preprocessor and postprocessor are now external
preprocessor = PolicyProcessorPipeline.from_pretrained("your-model-repo", config_filename="preprocessor_config.json")
postprocessor = PolicyProcessorPipeline.from_pretrained("your-model-repo", config_filename="postprocessor_config.json")
policy = get_policy_class("your-policy-type").from_pretrained("your-model-repo")

# Process data through the pipeline
processed_batch = preprocessor(raw_batch)
action = policy(processed_batch)
final_action = postprocessor(action)
```

*Generated automatically by the LeRobot policy migration script*

README.md

@@ -1,13 +1,13 @@
 ---
-datasets: AdilZtn/grab_red_cube_test_25
+datasets: unknown
 library_name: lerobot
 license: apache-2.0
 model_name: act
 pipeline_tag: robotics
 tags:
-- act
 - lerobot
 - robotics
+- act
 ---
 
 # Model Card for act
@@ -31,7 +31,7 @@ Below is the short version on how to train and run inference/eval:
 ### Train from scratch
 
 ```bash
-python -m lerobot.scripts.train \
+lerobot-train \
   --dataset.repo_id=${HF_USER}/<dataset> \
   --policy.type=act \
   --output_dir=outputs/train/<desired_policy_repo_id> \
@@ -41,12 +41,12 @@ python -m lerobot.scripts.train \
   --wandb.enable=true
 ```
 
-*Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`.*
+_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
 
 ### Evaluate the policy/run inference
 
 ```bash
-python -m lerobot.record \
+lerobot-record \
   --robot.type=so100_follower \
   --dataset.repo_id=<hf_user>/eval_<dataset> \
   --policy.path=<hf_user>/<desired_policy_repo_id> \
@@ -59,4 +59,4 @@ Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a
 
 ## Model Details
 
-* **License:** apache-2.0
+- **License:** apache-2.0