Hi, I’m Jake Burns. This summer, I worked on RoboTail, a project focused on the active control of a piezoelectric fish tail for bio-inspired underwater robots. Unlike traditional eel-like robots that use passive tails and rely on fluid dynamics and pneumatic actuation, RoboTail uses piezoelectric actuation to directly control tail motion—offering improved precision and adaptability in aquatic locomotion. We used color tracking via camera to monitor tail displacement under various input voltages and frequencies, successfully replicating commanded motion profiles. However, accuracy declined at higher frequencies due to camera framerate limitations. To evaluate propulsion performance, we submerged the tail and used a force-torque sensor to record thrust output. Results showed optimal thrust occurred at 100V and 12Hz, generating an average force of 91.46 mN. While controlling a piezoelectric tail in isolation is manageable, integrating it into a full soft robot presents increased complexity. To address this, we propose using reinforcement learning to discover efficient control policies for future implementation.