Multi-Mode Pneumatic Artificial Muscles Driven by Hybrid Positive-Negative Pressure
- URL: http://arxiv.org/abs/2603.15066v1
- Date: Mon, 16 Mar 2026 10:20:58 GMT
- Title: Multi-Mode Pneumatic Artificial Muscles Driven by Hybrid Positive-Negative Pressure
- Abstract summary: This paper introduces an architecture for Inflatable Fluid-Driven Origami-Inspired Artificial Muscles (IN-FOAMs)<n>A typical IN-FOAM consists of an inflatable skeleton enclosed within an outer skin, which can be driven using a combination of positive and negative pressures.<n>The skeleton patterns are programmable, enabling a variety of motions, including contracting, bending, twisting, and rotating.
- Score: 11.862266769445684
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Artificial muscles embody human aspirations for engineering lifelike robotic movements. This paper introduces an architecture for Inflatable Fluid-Driven Origami-Inspired Artificial Muscles (IN-FOAMs). A typical IN-FOAM consists of an inflatable skeleton enclosed within an outer skin, which can be driven using a combination of positive and negative pressures (e.g., compressed air and vacuum). IN-FOAMs are manufactured using low-cost heat-sealable sheet materials through heat-pressing and heat-sealing processes. Thus, they can be ultra-thin when not actuated, making them flexible, lightweight, and portable. The skeleton patterns are programmable, enabling a variety of motions, including contracting, bending, twisting, and rotating, based on specific skeleton designs. We conducted comprehensive experimental, theoretical, and numerical studies to investigate IN-FOAM's basic mechanical behavior and properties. The results show that IN-FOAM's output force and contraction can be tuned through multiple operation modes with the applied hybrid positive-negative pressure. Additionally, we propose multilayer skeleton structures to enhance the contraction ratio further, and we demonstrate a multi-channel skeleton approach that allows the integration of multiple motion modes into a single IN-FOAM. These findings indicate that IN-FOAMs hold great potential for future applications in flexible wearable devices and compact soft robotic systems.
Related papers
- Fabric Pneumatic Artificial Muscles Based on the Drawstring Principle [10.313921711241436]
This paper proposes a novel drawstring fabric pneumatic artificial muscle (DPAM)<n>Unlike traditional counterparts, the proposed DPAM produces no extra radial deformation during contraction, greatly improving structural compactness.<n>The DPAM exhibits outstanding mechanical performance: a load capacity over 800 times its self-weight, a maximum contraction ratio of 44%, and a power density up to 4.98 kW/kg.
arXiv Detail & Related papers (2026-07-21T02:20:38Z) - Guava: An Effective and Universal Harness for Embodied Manipulation [74.34187069605844]
We develop an end-to-end training pipeline that distills embodied manipulation capabilities into a 4B open-source model.<n>Results suggest that a well-designed harness can serve as a scalable, model-agnostic interface for embodied manipulation.
arXiv Detail & Related papers (2026-06-16T18:09:26Z) - A Compact Peristaltic Pump Based on Magneto-Elastic Hysteresis with Single Pneumatic Control [0.8121462458089143]
peristaltic pumps enable efficient and safe fluid transport by deforming an elastic tube without direct contact with the working fluid.<n>We present a soft membrane pump that achieves peristaltic motion through a single pneumatic input combined with an embedded passive magnet.
arXiv Detail & Related papers (2026-04-23T14:33:31Z) - Elastomeric Strain Limitation for Design of Soft Pneumatic Actuators [0.0]
This thesis presents strategies for the design, modeling, and strain-based control of human-safe elastomeric soft pneumatic actuators.<n>We investigate electroadhesive (EA) strain limiters for variable shape generation, rapid force application, and targeted inflation trajectories.<n>We validate theoretical models based on material properties and energy minimization using active learning and automated testing.
arXiv Detail & Related papers (2026-04-03T00:56:14Z) - Programmable Deformation Design of Porous Soft Actuator through Volumetric-Pattern-Induced Anisotropy [1.3464152928754487]
A novel design method is presented to realize soft porous actuators with programmable deformation by incising specific patterns into the porous foam body.<n>Our work provides a new, efficient, and scalable paradigm for the design of multi-functional soft porous robots.
arXiv Detail & Related papers (2025-12-13T13:17:17Z) - Active Learning Design: Modeling Force Output for Axisymmetric Soft Pneumatic Actuators [10.184372801256835]
Soft pneumatic actuators (SPA) made from elastomeric materials can provide large strain and large force.<n>We model force-pressure-height relationships for a concentrically strain-limited class of soft pneumatic actuators.<n>We show that this learned material model outperforms the theory-based model and naive curve-fitting approaches.
arXiv Detail & Related papers (2025-04-01T19:43:00Z) - An Untethered Bioinspired Robotic Tensegrity Dolphin with Multi-Flexibility Design for Aquatic Locomotion [26.745400349653767]
This paper presents the first steps toward a soft dolphin robot using a bio-inspired approach to mimic dolphin flexibility.
The current dolphin robot uses a minimalist approach, with only two actuated cable-driven degrees of freedom actuated by a pair of motors.
The actuated tail moves up and down in a swimming motion, but this first proof of concept does not permit controlled turns of the robot.
arXiv Detail & Related papers (2024-11-01T04:05:24Z) - Data-driven low-dimensional model of a sedimenting flexible fiber [0.0]
This work describes a data-driven technique to create high-fidelity low-dimensional models of flexible fiber dynamics using machine learning.
The approach combines an autoencoder neural network architecture to learn a low-dimensional latent representation of the filament shape.
We show that our data-driven model can accurately forecast the evolution of a fiber at both trained and untrained elasto-gravitational numbers.
arXiv Detail & Related papers (2024-05-16T21:07:09Z) - MS-MANO: Enabling Hand Pose Tracking with Biomechanical Constraints [50.61346764110482]
We integrate a musculoskeletal system with a learnable parametric hand model, MANO, to create MS-MANO.
This model emulates the dynamics of muscles and tendons to drive the skeletal system, imposing physiologically realistic constraints on the resulting torque trajectories.
We also propose a simulation-in-the-loop pose refinement framework, BioPR, that refines the initial estimated pose through a multi-layer perceptron network.
arXiv Detail & Related papers (2024-04-16T02:18:18Z) - A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics [73.35846234413611]
In drug discovery, molecular dynamics (MD) simulation provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites.
We propose NeuralMD, the first machine learning (ML) surrogate that can facilitate numerical MD and provide accurate simulations in protein-ligand binding dynamics.
We demonstrate the efficiency and effectiveness of NeuralMD, achieving over 1K$times$ speedup compared to standard numerical MD simulations.
arXiv Detail & Related papers (2024-01-26T09:35:17Z) - DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative
Diffusion Models [102.13968267347553]
We present DiffuseBot, a physics-augmented diffusion model that generates soft robot morphologies capable of excelling in a wide spectrum of tasks.
We showcase a range of simulated and fabricated robots along with their capabilities.
arXiv Detail & Related papers (2023-11-28T18:58:48Z) - Robust Hamiltonian Engineering for Interacting Qudit Systems [50.591267188664666]
We develop a formalism for the robust dynamical decoupling and Hamiltonian engineering of strongly interacting qudit systems.
We experimentally demonstrate these techniques in a strongly-interacting, disordered ensemble of spin-1 nitrogen-vacancy centers.
arXiv Detail & Related papers (2023-05-16T19:12:41Z) - Conditional Generative Models for Simulation of EMG During Naturalistic
Movements [45.698312905115955]
We present a conditional generative neural network trained adversarially to generate motor unit activation potential waveforms.
We demonstrate the ability of such a model to predictively interpolate between a much smaller number of numerical model's outputs with a high accuracy.
arXiv Detail & Related papers (2022-11-03T14:49:02Z) - Differentiable Simulation of Soft Multi-body Systems [99.4302215142673]
We develop a top-down matrix assembly algorithm within Projective Dynamics.
We derive a differentiable control framework for soft articulated bodies driven by muscles, joint torques, or pneumatic tubes.
arXiv Detail & Related papers (2022-05-03T20:03:22Z) - Learning physics-informed simulation models for soft robotic
manipulation: A case study with dielectric elastomer actuators [21.349079159359746]
Soft actuators offer a safe and adaptable approach to robotic tasks like gentle grasping and dexterous movement.
Creating accurate models to control such systems is challenging due to the complex physics of deformable materials.
This paper presents a framework that combines the advantages of differentiable simulator and Finite Element Method.
arXiv Detail & Related papers (2022-02-25T21:15:05Z) - Learning Material Parameters and Hydrodynamics of Soft Robotic Fish via
Differentiable Simulation [26.09104786491426]
Our framework allows high fidelity prediction of dynamic behavior for composite bi-morph bending structures in real hardware.
We demonstrate an experimentally-verified, fast optimization pipeline for learning the material parameters and hydrodynamics of our robots.
Although we focus on a specific application for underwater soft robots, our framework is applicable to any pneumatically actuated soft mechanism.
arXiv Detail & Related papers (2021-09-30T05:24:02Z) - QuTiP-BoFiN: A bosonic and fermionic numerical
hierarchical-equations-of-motion library with applications in
light-harvesting, quantum control, and single-molecule electronics [51.15339237964982]
"hierarchical equations of motion" (HEOM) is a powerful exact numerical approach to solve the dynamics.
It has been extended and applied to problems in solid-state physics, optics, single-molecule electronics, and biological physics.
We present a numerical library in Python, integrated with the powerful QuTiP platform, which implements the HEOM for both bosonic and fermionic environments.
arXiv Detail & Related papers (2020-10-21T07:54:56Z)
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.