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Utilities for extracting normalization layers from model weights into external pipelines.
Distinct from Normalization Layers: Distinct from general normalization layers: focuses on the migration of layers out of model weights.
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This project is a comprehensive research platform designed for the end-to-end lifecycle of robotic learning. It provides a modular framework for training neural network policies—specifically through imitation and reinforcement learning—and deploying them onto physical robotic hardware. By offering a unified interface for hardware abstraction, the platform decouples high-level control logic from the specific sensors and actuators of diverse robotic systems. The framework distinguishes itself through a standardized approach to data and policy management. It utilizes a consistent schema for reco
Extracts normalization layers from trained model weights into external processor pipelines.