MORPH Wheel: A Passive Variable-Radius Wheel Embedding Mechanical Behavior Logic for Input-Responsive Transformation
- URL: http://arxiv.org/abs/2602.06265v1
- Date: Thu, 05 Feb 2026 23:46:26 GMT
- Title: MORPH Wheel: A Passive Variable-Radius Wheel Embedding Mechanical Behavior Logic for Input-Responsive Transformation
- Abstract summary: This paper introduces the MORPH wheel, a fully passive variable-radius wheel that embeds mechanical behavior logic for torque-responsive transformation.<n>The design integrates a torque-response coupler and spring-loaded connecting struts to mechanically adjust the wheel radius.<n>The MORPH wheel exemplifies a mechanically programmed structure, embedding intelligent, context-dependent behavior directly into its physical design.
- Score: 7.186456094701819
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper introduces the Mechacnially prOgrammed Radius-adjustable PHysical (MORPH) wheel, a fully passive variable-radius wheel that embeds mechanical behavior logic for torque-responsive transformation. Unlike conventional variable transmission systems relying on actuators, sensors, and active control, the MORPH wheel achieves passive adaptation solely through its geometry and compliant structure. The design integrates a torque-response coupler and spring-loaded connecting struts to mechanically adjust the wheel radius between 80 mm and 45 mm in response to input torque, without any electrical components. The MORPH wheel provides three unique capabilities rarely achieved simultaneously in previous passive designs: (1) bidirectional operation with unlimited rotation through a symmetric coupler; (2) high torque capacity exceeding 10 N with rigid power transmission in drive mode; and (3) precise and repeatable transmission ratio control governed by deterministic kinematics. A comprehensive analytical model was developed to describe the wheel's mechanical behavior logic, establishing threshold conditions for mode switching between direct drive and radius transformation. Experimental validation confirmed that the measured torque-radius and force-displacement characteristics closely follow theoretical predictions across wheel weights of 1.8-2.8kg. Robot-level demonstrations on varying loads (0-25kg), slopes, and unstructured terrains further verified that the MORPH wheel passively adjusts its radius to provide optimal transmission ratio. The MORPH wheel exemplifies a mechanically programmed structure, embedding intelligent, context-dependent behavior directly into its physical design. This approach offers a new paradigm for passive variable transmission and mechanical intelligence in robotic mobility systems operating in unpredictable or control-limited environments.
Related papers
- Friction Characterization of a Cable-Driven Differential Actuation System for Lower-Limb Exoskeletons [42.628421392139]
Lower-limb exoskeletons require actuation systems that can provide accurate joint torque control.<n> Conventional architectures often rely on independently actuated joints and joint-level torque sensors.<n>This paper presents a novel differential actuation architecture for hip-knee flexion/extension.
arXiv Detail & Related papers (2026-06-14T19:47:32Z) - Identification of a Physics-Based Electrical Power Consumption Model for the Unitree G1 Humanoid Arm [41.90412270641482]
This letter presents a physics-based, linear-in- parameters model for the electrical power consumption of the left arm of the UnitreeG1 humanoid robot.<n>The proposed formulation combines actuator loss terms with a baseline-torque correction that captures changes in gravity-compensation load.<n>Across 897 trajectories covering single-joint and coordinated arm motions at multiple speed levels, the identified model achieves $R2 = 0.933$ with an RMSE of 1.07 (W)
arXiv Detail & Related papers (2026-06-14T16:45:07Z) - Driver-WM: A Driver-Centric Traffic-Conditioned Latent World Model for In-Cabin Dynamics Rollout [55.19244743715421]
Driver-WM is a driver-centric latent world model that rolls out in-cabin dynamics causally conditioned on out-cabin traffic context.<n>It unifies physical kinematics forecasting with auxiliary behavioral and emotional semantic recognition.<n>Driver-WM yields robust long-horizon geometric forecasting for reactive high-motion maneuvers.
arXiv Detail & Related papers (2026-05-06T16:30:48Z) - Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing [73.88859384645264]
Out-Of-Distribution (OOD) generalization to various racetracks utilizes Machine Learning (ML) to encode the mathematical relation between sensor data and vehicle actuation for end-to-end control.<n>This paper presents a DRL method that parameterizes nonlinear vehicle dynamics from the spectral distribution of depth measurements with a non-geometric, physics-informed reward, to infer vehicle time-optimal and overtaking racing controls with an Artificial Neural Network (ANN)<n>The policy outperforms human demonstrations by 12% in OOD tracks on proportionally scaled hardware, by maximizing the friction circle with tire dynamics that resemble an empirical Pacejka tire model.
arXiv Detail & Related papers (2026-04-10T17:12:07Z) - Design of an In-Pipe Robot with Contact-Angle-Guided Kinematic Decoupling for Crosstalk-Suppressed Locomotion [20.379309593600414]
In-pipe inspection robots must traverse confined pipeline networks with elbows and three-dimensional fittings.<n>In compact V-shaped platforms, these functions often rely on shared contacts or indirect actuation.<n>This paper presents a V-shaped in-pipe robot with a joint-axis-and-wheel-separation layout that provides two physically independent actuation channels.
arXiv Detail & Related papers (2026-03-28T11:40:36Z) - Geometry-based pneumatic actuators for soft robotics [3.516880783923843]
We present geometry-based pneumatic actuators (GPAs), a design and implementation approach that introduces constraint layers with CNC heat-sealed chambers.<n>The approach achieves predictable deformation, near-zero bending radii, multi-states actuation, and enables customizable and repeatable complex actuated geometries.<n>We demonstrate versatility of the GPAs approach through three applications: a 49 g wrist exoskeleton reducing muscle activity by up to 51%, a 30.8 g haptic interface delivering 8 N force feedback with fast response, and a 208 g bipedal robot achieving multi-gait locomotion.
arXiv Detail & Related papers (2026-02-27T15:43:22Z) - Hybrid Offline-Online Reinforcement Learning for Sensorless, High-Precision Force Regulation in Surgical Robotic Grasping [2.874057693956189]
We present a sensorless control framework that combines physics-consistent modeling and hybrid reinforcement learning.<n>We develop a first-principles digital twin of the da Vinci Xi grasping mechanism that captures coupled electrical, transmission, and jaw dynamics.<n>In simulation, the controller maintains grasp force within 1% of the desired reference during multi-harmonic jaw motion.
arXiv Detail & Related papers (2026-02-27T10:11:59Z) - Physics-Embedded Neural ODEs for Learning Antagonistic Pneumatic Artificial Muscle Dynamics [2.8169681659450876]
Pneumatic artificial muscles (PAMs) enable compliant actuation for soft wearable, assistive, and interactive robots.<n>This paper presents a hybrid neural ordinary differential equation framework that embeds physical structure into a learned model of antagonistic PAM dynamics.
arXiv Detail & Related papers (2026-02-27T04:38:22Z) - Snapping Actuators with Asymmetric and Sequenced Motion [6.7646973106436254]
A compact quadrupedal robot achieves coordinated wavelike locomotion using only a single pressure input.<n>The robot exhibits frequency-dependent performance with a maximum speed of 72.78mm/s at 7.5Hz.
arXiv Detail & Related papers (2026-02-20T18:45:17Z) - Tendon-Driven Reciprocating and Non-Reciprocating Motion via Snapping Metabeams [0.0]
A tendon-driven mechanism consisting of spiral-based metabeams was developed for producing both reciprocating and non-reciprocating motion.<n>The spiral geometry allows large reversible deformation even when made from a relatively stiff material such as PLA.<n>The developed mechanism was further integrated into a swimming robot, where tendon-driven fins exhibited two distinct actuation modes.
arXiv Detail & Related papers (2026-02-20T16:35:51Z) - 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) - SPINE Gripper: A Twisted Underactuated Mechanism-based Passive Mode-Transition Gripper [9.01638832898787]
Single-actuator passive gripper achieves stable grasping and continuous bidirectional in-hand rotation through mechanically encoded power transmission logic.<n>Key enabler of this behavior is a Twisted Underactuated Mechanism (TUM), which generates non-coplanar motions.
arXiv Detail & Related papers (2026-01-11T09:55:18Z) - DSO-VSA: a Variable Stiffness Actuator with Decoupled Stiffness and Output Characteristics for Rehabilitation Robotics [11.174959557114532]
This paper presents a variable stiffness actuator featuring decoupled stiffness and output behavior for rehabilitation robotics.<n>The system integrates a variable stiffness mechanism that combines a variable-length lever with a hypocycloidal straight-line mechanism to achieve a linear torque-deflection relationship.<n>A cascade PI controller is further developed on the basis of the differential configuration, in which the position-loop term jointly regulates stiffness and deflection angle.
arXiv Detail & Related papers (2025-12-21T12:13:17Z) - Forecasting Continuous Non-Conservative Dynamical Systems in SO(3) [51.510040541600176]
We propose a novel approach to modeling the rotation of moving objects in computer vision.<n>Our approach is agnostic to energy and momentum conservation while being robust to input noise.<n>By learning to approximate object dynamics from noisy states during training, our model attains robust extrapolation capabilities in simulation and various real-world settings.
arXiv Detail & Related papers (2025-08-11T09:03:10Z) - PIN-WM: Learning Physics-INformed World Models for Non-Prehensile Manipulation [44.44501164377334]
We learn a world model of the 3D rigid body dynamics involved in non-prehensile manipulations.<n> PIN-WM can be learned with only few-shot and task-agnostic physical interaction trajectories.<n>We turn the learned PIN-WM into a group of Digital Cousins via physics-aware randomizations.
arXiv Detail & Related papers (2025-04-23T13:27:07Z) - RACER: Rational Artificial Intelligence Car-following-model Enhanced by Reality [46.909086734963665]
This paper introduces RACER, a cutting-edge deep learning car-following model to predict Adaptive Cruise Control (ACC) driving behavior.<n>Unlike conventional models, RACER effectively integrates Rational Driving Constraints (RDCs), crucial tenets of actual driving.<n> RACER excels across key metrics, such as acceleration, velocity, and spacing, registering zero violations.
arXiv Detail & Related papers (2023-12-12T06:21:30Z) - Cat states in a driven superfluid: role of signal shape and switching
protocol [62.997667081978825]
We investigate the behavior of a one-dimensional Bose-Hubbard model whose kinetic energy is made to oscillate with zero time-average.
We analyze the robustness of this unconventional ground state against variations of a number of system parameters.
arXiv Detail & Related papers (2020-05-11T15:15:06Z)
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.