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