CFG-Policy: Unified Diffusion Policy Learning Framework via Classifier-free Guidance
Overview
CFG-Policy introduces a unified framework for diffusion policy learning, separating exploration and exploitation. It combines score or velocity fields using classifier-free guidance to enhance task performance and behavioral diversity across MuJoCo and RoboTwin tasks.
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- arXiv Robotics — research abstractsCFG-Policy: Unified Diffusion Policy Learning Framework via Classifier-free Guidance
This paper presents CFG-Policy, a unified and plug-and-play framework for diffusion policy learning that separates exploration and exploitation into two generative policies. By combining their score or velocity fields through classifier-free guidance, the framework aims to improve task performance while preserving behavioral diversity. Results are shown on 5 MuJoCo tasks using QSM and QVPO, and on 4 RoboTwin manipulation tasks using Flow-SDE+PPO.
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