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

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).

Figures (12)

• Figure 1: The Ultra Mobility Vehicle (UMV) and its athletic repertoire. (A) Diagram of UMV, a bicycle-based robot with five actuated degrees of freedom [Links (green) and subcomponents (purple)]. It has steering and rear-wheel drive actuators for basic ground mobility. The design concentrates most of the robot's mass in the Head, which connects to the Bike through a spatial linkage. This linkage consists of the Neck and two tie rods. Powerful actuators in the Head work through the linkage to let the robot "throw" its mass around, enabling dynamic behaviors. Composite images of (B) a front flip, demonstrating high pitch angular momentum and modulation of inertia via body tucking, (C) rear-wheel hopping, where the robot maintains balance like a single-legged hopper, (D) an autonomous table jump sequence, where the robot accelerates, vaults onto a 1-meter platform, traverses it, and lands stably.
• Figure 2: combines the bicycle form with legged agility, allowing for athletic motions, including: (1) track-standing, in which the robot balances in-place using only wheel and steering actuation; (2) backward driving, (3) "shimmy-turning," in which the robot lifts the front wheel to yaw in-place about the rear wheel, (4) lateral hopping on a single wheel, (5) front flipping, and (6) high-clearance table jumping.
• Figure 3: Kinematic architecture and actuation strategy. (A) Side view showing joint layout. Joints $q_h$, $q_l$, and $q_r$ actuate the spatial linkage, allowing the Head to shift its mass relative to the Bike link. The design parameter $\hat{\psi}$ represents the fixed angle of the out-of-plane joint $\phi$ axis. (B) Detailed view of the spatial mechanism and actuator placement. Actuators $A_0$ through $A_3$ are remotized to the Head to maximize the center of mass height and minimize leg inertia for jumping. $A_0$ and $A_1$ are mechanically coupled to drive the $\phi$ joint, providing lateral authority for single-wheel balancing.
• Figure 4: Positions, footstep locations, and headings over a roughly 12-second period of lateral hopping on hardware.$p_{\text{CoM}}$ shows the oscillation of the whole-body center of mass throughout the hopping motion. The traces at ground level ($p_{\text{clearance}}$) indicate the lowest point of the rear wheel in both flight and stance. These visualize a learned stabilization strategy where the rear wheel rolls backward and forward during the stance phase to maintain balance, analogous to a unicycle.
• Figure 5: Energetics and repeatability of high-clearance jumping. (A) System performance during a 1-meter table jump. The top plot shows the CoM height ($h_{\text{CoM}}$) and rear wheel clearance ($h_{\text{clearance}}$). The vertical dashed lines mark the high-impulse lift-off and landing impact events. The bottom plots illustrate the electrical demand; the system draws peak power (approx. 4.5 kW) immediately prior to lift-off, causing a significant voltage sag, while landing impacts briefly drive the actuators into regeneration (negative power). (B) Spatial repeatability of the rear wheel trajectory ($p_{\text{clearance}}$) across 15 consecutive autonomous jumps. The tight clustering of trajectories during the ascent phase demonstrates the precision of the learned policy, with increased variance occurring only post-impact during the drop-landing.