System Design of the Ultra Mobility Vehicle: A Driving, Balancing, and Jumping Bicycle Robot

TL;DR

This paper presents the design and control of the Ultra Mobility Vehicle, a versatile robotic bicycle capable of jumping, balancing, and driving, achieved through simulation-driven optimization and reinforcement learning for diverse athletic behaviors.

Contribution

It introduces a novel robotic platform combining a bicycle with a reaction mass, optimized via simulation and controlled with reinforcement learning for complex dynamic maneuvers.

Findings

Capable of speeds up to 8 m/s

Can jump over obstacles 1 meter tall

Demonstrates diverse athletic behaviors with zero-shot transfer

Abstract

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).