The recent advancements in the machine learning field have demonstrated the potential of knowledge transfer and multi-task learning to enhance the performance and generalization capabilities of models across various domains. In the context of robotics, the ability to transfer skills between different embodiments is particularly appealing, as it can significantly reduce the time and resources required for training control agents, while also improving their adaptability and robustness. This thesis investigates how to leverage demonstrations and Reinforcement Learning (RL) to train agents capable of solving diverse manipulation tasks using multiple robotic arms and grippers. The proposed method utilizes a contrastive supervised learning approach to construct a shared representation of different robotic configurations and tasks, aligning state and action spaces across diverse embodiments while preserving the critical distinctions necessary for accurate task execution.