Designing generalizable agents capable of adapting to diverse embodiments has
achieved significant attention in Reinforcement Learning (RL), which is
critical for deploying RL agents in various real-world applications. Previous
Cross-Embodiment RL approaches have focused on transferring knowledge across
embodiments within specific tasks. These methods often