The Ultra Mobile Vehicle (UMV), developed by the Robotics and AI Institute, is a two-wheeled autonomous robotic bicycle designed to perform advanced stunts and maneuvers. Using reinforcement learning, the UMV can drive, turn, jump on tables, perform wheelies, and execute precise stops known as track-stands. It balances dynamically by steering with its front wheel, eliminating the need for gyroscopic systems. These capabilities are achieved through reinforcement learning, where the robot learns from trial-and-error interactions with its environment without human intervention.

The UMV showcases its agility by performing tricks such as bunny hops and backward riding while maintaining stability during landings and sudden stops. This technology highlights the potential of reinforcement learning in robotics, enabling machines to refine their movements over time based on environmental feedback. The UMV’s versatility suggests applications in search-and-rescue operations, military reconnaissance, and robotics research. Its ability to handle complex maneuvers autonomously represents a significant advancement in robotic performance.