This paper proposes a meta-learning and reinforcement-learning based control system ”Cerebellum” for robotic arms to address the issue of adapting to hardware parameter changes. By leveraging the con- cept of meta-learning, the system achieves adaptive control by learn- ing from few samples. Experimental results show our ”Cerebellum” reach 90.09% accuracy in a simulation environment with control- ling newly unseen robotic arm. However, challenges remain in hard- ware experiments due to image distortions and Mechanical collide issues. Further research is needed to overcome these challenges.