WaveForward: An Omnidirectional Passive Wheeled Quadruped Robot with Casters

• URL: http://arxiv.org/abs/2606.25299v1
• Date: Wed, 24 Jun 2026 01:59:51 GMT
• Title: WaveForward: An Omnidirectional Passive Wheeled Quadruped Robot with Casters
• Abstract summary: Wheeled-legged robots possess both agile mobility for traversing complex terrains and high efficiency, making them suitable for long-distance transportation applications.<n>We propose a novel and low-cost passive wheeled legged robot equipped with standard casters on each leg to obtain omnidirectional mobility.
• Score: 7.753040656802682
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Wheeled-legged robots possess both agile mobility for traversing complex terrains and high efficiency, making them suitable for long-distance transportation applications. Conventional actuated wheeled robots require specialized hardware and electrical design due to the incorporation of wheel components. We propose a novel and low-cost passive wheeled legged robot equipped with standard casters on each leg to obtain omnidirectional mobility. The control method employs an asymmetric actor-critic structure, enabling the utilization of the privileged information of the passive caster's angles and velocities. We develop a caster base posture adjustment strategy based on velocity commands, utilizing actuated joints to modify the caster base joint axis posture and thereby adjust the propulsion direction of the casters. Moreover, we implemented multiple propulsion modes to achieve varying degrees of caster twisting oscillation, converting these into propulsive force. We conducted a slalom test and mode switch experience, which shows the passive wheeled quadruped could achieve omnidirectional movement versatility, and reduce the cost of transport (COT) by up to 89.1% with respect to legged motion.

Related papers

• A Reconfigurable Rocker-Bogie Robot for High Step Climbing and Turning [14.083125131628549]
  Reconfigurable rocker-bogie mechanism achieves efficient turning motion with a small number of actuators.<n>System enables switching between four-wheel and six-wheel configurations.<n>Prototype robot successfully climbed a 40 cm step with an average climbing time of 6.4 s.
  arXiv  Detail & Related papers  (2026-07-02T00:22:50Z)
• Reinforcement Learning-Based Control for an Inline Skating Humanoid Robot [10.006591553493807]
  We train and deploy a reinforcement learning control policy that enables novel locomotion strategies for a humanoid robot modified to equip consumer inline skates instead of conventional feet.<n>Our skating strategies emerge entirely from our reward structure, without reliance on human motion data, imitation learning, or kinematic priors.<n>Our policy demonstrates up to a 50% reduction in Cost of Transport compared to standard walking gaits.
  arXiv  Detail & Related papers  (2026-06-30T15:25:06Z)
• KYON: Semi-Modular Wheel-Legged Quadruped With Agile Bimanual Capability [3.041420062518373]
  KYON is a wheel-legged quadruped robot equipped with a bimanual upper body for loco-manipulation tasks.<n>The platform features a semi-modular design with a reconfigurable lower legs, enabling both wheeled and legged locomotion depending on the environment.
  arXiv  Detail & Related papers  (2026-06-29T12:54:09Z)
• X2-N: A Transformable Wheel-legged Humanoid Robot with Dual-mode Locomotion and Manipulation [12.614951789242896]
  We present X2-N, a high-DoF transformable robot with dual-mode locomotion and manipulation.<n>X2-N can operate in both humanoid and wheel-legged forms and transform seamlessly between them through joint reconfiguration.<n>We validate X2-N in a range of challenging locomotion and manipulation tasks, including dynamic skating-like motion, stair climbing and package delivery.
  arXiv  Detail & Related papers  (2026-04-23T11:10:11Z)
• Efficient and Versatile Quadrupedal Skating: Optimal Co-design via Reinforcement Learning and Bayesian Optimization [8.969390069418777]
  We present a hardware-control co-design approach that enables efficient and versatile roller skating on quadrupedal robots equipped with passive wheels.<n> Passive-wheel skating reduces leg inertia and improves energy efficiency, particularly at high speeds.
  arXiv  Detail & Related papers  (2026-03-19T02:12:50Z)
• 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)
• System Design of the Ultra Mobility Vehicle: A Driving, Balancing, and Jumping Bicycle Robot [20.113840669330596]
  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.
  arXiv  Detail & Related papers  (2026-02-25T17:05:09Z)
• 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)
• 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)
• 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)
• VAE-Loco: Versatile Quadruped Locomotion by Learning a Disentangled Gait Representation [78.92147339883137]
  We show that it is pivotal in increasing controller robustness by learning a latent space capturing the key stance phases constituting a particular gait. We demonstrate that specific properties of the drive signal map directly to gait parameters such as cadence, footstep height and full stance duration. The use of a generative model facilitates the detection and mitigation of disturbances to provide a versatile and robust planning framework.
  arXiv  Detail & Related papers  (2022-05-02T19:49:53Z)
• Next Steps: Learning a Disentangled Gait Representation for Versatile Quadruped Locomotion [69.87112582900363]
  Current planners are unable to vary key gait parameters continuously while the robot is in motion. In this work we address this limitation by learning a latent space capturing the key stance phases constituting a particular gait. We demonstrate that specific properties of the drive signal map directly to gait parameters such as cadence, foot step height and full stance duration.
  arXiv  Detail & Related papers  (2021-12-09T10:02:02Z)
• Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots [121.42930679076574]
  We present a model-free reinforcement learning framework for training robust locomotion policies in simulation. domain randomization is used to encourage the policies to learn behaviors that are robust across variations in system dynamics. We demonstrate this on versatile walking behaviors such as tracking a target walking velocity, walking height, and turning yaw.
  arXiv  Detail & Related papers  (2021-03-26T07:14:01Z)

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.