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Utilities for scaling raw action values for training and recovering original values during inference.
Distinct from Data Normalization: Distinct from general data normalization: focuses on action space scaling for robotic control.
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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
Scales raw action values into a normalized range for model training and inference.