Trials cyclists and mountain bike riders can hop, jump, balance, and drive on one or both wheels. This versatility allows them to achieve speed and energy-efficiency on smooth terrain and agility over rough terrain. Inspired by these athletes, we present the design and control of a robotic platform, Ultra Mobility Vehicle (UMV), which combines a bicycle and a reaction mass to move dynamically with minimal actuated degrees of freedom. We employ a simulation-driven design optimization process to synthesize a spatial linkage topology with a focus on vertical jump height and momentum-based balancing on a single wheel contact. Using a constrained Reinforcement Learning (RL) framework, we demonstrate zero-shot transfer of diverse athletic behaviors, including track-stands, jumps, wheelies, rear wheel hopping, and front flips. This 23.5 kg robot is capable of high speeds (8 m/s) and jumping on and over large obstacles (1 m tall, or 130% of the robot's nominal height).

翻译：试验自行车手与山地车骑手能够实现单轮或双轮的跳跃、弹跳、平衡与行驶。这种多功能性使其在平坦地形上实现高速与高能效，在崎岖地形上保持灵活机动。受此类运动员的启发，本文提出一种机器人平台——超机动车辆（UMV）的设计与控制方案，该平台结合自行车结构与反作用质量块，以最少的驱动自由度实现动态运动。我们采用仿真驱动的设计优化流程，综合构建了一种空间连杆拓扑结构，重点优化垂直跳跃高度及基于单轮接触的动量平衡能力。通过约束强化学习（RL）框架，我们实现了多种运动行为的零样本迁移，包括定车平衡、跳跃、翘轮、后轮弹跳及前空翻。该机器人重23.5公斤，可实现高速运动（8米/秒），并能跃上或越过大型障碍物（高度达1米，相当于机器人标称高度的130%）。