Robotic policy training can be slow and tedious but with imitation learning the process can be sped up tremendously. Imitation learning models such as ACT have millions of parameters fewer than the massive billion parameter reinforcement learning models and Vision Language action models currently used industry-wide. We discuss our results of training ACT policies on consumer grade hardware, testing our results on the Standard Open 101 arm with and intel RealSense depth camera, and what it means for the future of robotic policy training.