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

2026-02-25

AI Summary

This work proposes a lightweight single-wheel bicycle robot capable of versatile dynamic maneuvers inspired by extreme cycling, addressing the challenge of agile locomotion over complex terrain. By integrating topology-optimized spatial linkages with a momentum-based single-wheel balancing mechanism, the design achieves high-performance behaviors—including high-speed riding, static balancing, wheelies, bunny hops, and front flips—using minimal actuation degrees of freedom. A simulation-driven co-optimization framework jointly refines the mechanical structure and a constrained reinforcement learning control policy, enabling zero-shot transfer of multiple dynamic skills to the physical robot for the first time. The resulting Ultra Mobility Vehicle weighs 23.5 kg, reaches speeds up to 8 m/s, and can surmount obstacles 1 m high—130% of its own height.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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).

Problem

• bicycle robot
• dynamic mobility
• balancing
• jumping
• minimal actuation

Innovation

• bicycle robot
• simulation-driven design
• constrained reinforcement learning
• zero-shot transfer
• dynamic mobility