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

• URL: http://arxiv.org/abs/2602.22118v3
• Date: Tue, 17 Mar 2026 16:19:59 GMT
• Title: System Design of the Ultra Mobility Vehicle: A Driving, Balancing, and Jumping Bicycle Robot
• Abstract summary: Trials cyclists and mountain bike riders can hop, jump, balance, and drive on one or both wheels.<n>This 23.5 kg robot is capable of high speeds and jumping on and over large obstacles.
• Score: 20.113840669330596
• License: http://creativecommons.org/licenses/by/4.0/
• 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).

Related papers

• Bicycle Acrobatics with Reinforcement Learning [3.1700350168889018]
  We use Reinforcement Learning to enable a bicycle robot to learn and compose a diverse repertoire of dynamic acrobatic stunts.<n>The robot acquires autonomous single and multi-table forward and lateral jumps, steerable jumps, front flips, kip-ups, kip-downs, driving, wheelies, bunny hops, and three-point turns.<n>We validate the approach on the Ultra Mobility Vehicle (UMV), a custom bicycle robot, in simulation and hardware.
  arXiv  Detail & Related papers  (2026-08-01T21:54:40Z)
• LIMBERO: A Limbed Climbing Exploration Robot Toward Traveling on Rocky Cliffs [2.374610323448344]
  LIMBERO is a 10 kg-class quadrupedal climbing robot with spine-type grippers on its feet.<n>We introduce a novel gripper design featuring coupled finger-closing and spine-hooking motions, tightly actuated by a single motor.<n>We develop an efficient algorithm to visualize a geometry-based graspability index on continuous rough terrain.
  arXiv  Detail & Related papers  (2026-03-17T13:53:45Z)
• Underactuated multimodal jumping robot for extraterrestrial exploration [0.3867363075280543]
  We present a rolling and jumping underactuated monopedal robot designed to explore multimodal locomotion on low-gravity bodies.<n>It uses only two reaction wheels to control its spatial orientation with two controllers: a balancing controller which can aim the robot's jump direction on the ground, and an aerial reorientation controller which can aim the robot's leg for landing after flight.
  arXiv  Detail & Related papers  (2026-03-06T18:08:32Z)
• Jumping Control for a Quadrupedal Wheeled-Legged Robot via NMPC and DE Optimization [14.88101223616473]
  Quadrupedal wheeled-legged robots combine the advantages of legged and wheeled locomotion to achieve superior mobility.<n> executing dynamic jumps remains a significant challenge due to the additional degrees of freedom introduced by wheeled legs.<n>This paper develops a mini-sized wheeled-legged robot for agile motion and presents a novel motion control framework.
  arXiv  Detail & Related papers  (2026-02-25T06:13:59Z)
• Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching [77.28042137892943]
  We present Perceptive Humanoid Parkour (PHP), a modular framework that enables humanoid robots to autonomously perform long-horizon, vision-based parkour.<n>We train motion-tracking reinforcement learning expert policies for these composed motions, and distill them into a single depth-based, multi-skill student policy.<n>We validate our framework with extensive real-world experiments on a Unitree G1 humanoid robot.
  arXiv  Detail & Related papers  (2026-02-17T18:59:11Z)
• HUSKY: Humanoid Skateboarding System via Physics-Aware Whole-Body Control [67.58927286226925]
  We propose a learning-based framework that integrates humanoid-skateboard system modeling and physics-aware whole-body control.<n>Experiments on the Unitree G1 humanoid platform demonstrate that our framework enables stable and agile maneuvering on skateboards in real-world scenarios.
  arXiv  Detail & Related papers  (2026-02-03T07:18:01Z)
• Humanoid Whole-Body Locomotion on Narrow Terrain via Dynamic Balance and Reinforcement Learning [54.26816599309778]
  We propose a novel whole-body locomotion algorithm based on dynamic balance and Reinforcement Learning (RL)<n> Specifically, we introduce a dynamic balance mechanism by leveraging an extended measure of Zero-Moment Point (ZMP)-driven rewards and task-driven rewards in a whole-body actor-critic framework.<n> Experiments conducted on a full-sized Unitree H1-2 robot verify the ability of our method to maintain balance on extremely narrow terrains.
  arXiv  Detail & Related papers  (2025-02-24T14:53:45Z)
• Impedance Matching: Enabling an RL-Based Running Jump in a Quadruped Robot [7.516046071926082]
  We propose a new framework to mitigate the gap between simulated and real robots. Our framework offers a structured guideline for parameter selection and the range for dynamics randomization in simulation. Results are, to the best of our knowledge, one of the highest and longest running jumps demonstrated by an RL-based control policy in a real quadruped robot.
  arXiv  Detail & Related papers  (2024-04-23T14:52:09Z)
• Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control [106.32794844077534]
  This paper presents a study on using deep reinforcement learning to create dynamic locomotion controllers for bipedal robots. We develop a general control solution that can be used for a range of dynamic bipedal skills, from periodic walking and running to aperiodic jumping and standing. This work pushes the limits of agility for bipedal robots through extensive real-world experiments.
  arXiv  Detail & Related papers  (2024-01-30T10:48:43Z)
• Barkour: Benchmarking Animal-level Agility with Quadruped Robots [70.97471756305463]
  We introduce the Barkour benchmark, an obstacle course to quantify agility for legged robots. Inspired by dog agility competitions, it consists of diverse obstacles and a time based scoring mechanism. We present two methods for tackling the benchmark.
  arXiv  Detail & Related papers  (2023-05-24T02:49:43Z)
• Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot [76.04391023228081]
  We address the problem of enabling quadrupedal robots to perform precise shooting skills in the real world using reinforcement learning. We propose a hierarchical framework that leverages deep reinforcement learning to train a robust motion control policy. We deploy the proposed framework on an A1 quadrupedal robot and enable it to accurately shoot the ball to random targets in the real world.
  arXiv  Detail & Related papers  (2022-08-01T22:34:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.