Robot engineers are constantly inspired by nature and past inventions to improve their designs. Take the Ultra Mobility Vehicle for instance: it is a versatile robot that can hop, jump, and balance like mountain bike riders. It combines a bicycle with a reactive robot that can perform jumps. This 23.5-kilogram robot is capable of high speeds and can jump over 1m tall.
While legged robots navigate unstructured environments well, their high energy use and low speeds can hinder longer journeys. Wheeled robots offer efficiency but can struggle with obstacles like curbs or stairs. To bridge this gap between speed, agility, and terrain versatility, we introduced the Ultra Mobility Vehicle (UMV), a single-track bicycle robot that combines wheeled efficiency with dynamic articulation.
Learn more about UMV’s system design and how Reinforcement Learning (RL) is applied to control bike jumps, flips, bunny hops, and more in the October 2026 issue of @SciRobotics https://t.co/c3Sdkq7LZp
— RAI Institute (@rai_inst) October 14, 2026
As researchers explain:
Using a deep reinforcement learning (RL) framework, we demonstrate zero-shot transfer of diverse athletic behaviors, including track stands, jumps, wheelies, rear-wheel hopping, and front flips.
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