Fluidically Innervated Lattices Make Versatile and Durable Tactile Sensors

• URL: http://arxiv.org/abs/2507.21225v1
• Date: Mon, 28 Jul 2025 18:00:04 GMT
• Title: Fluidically Innervated Lattices Make Versatile and Durable Tactile Sensors
• Abstract summary: We introduce a passive soft robotic fingertip with integrated tactile sensing, fabricated using a 3D-printed elastomer lattice with embedded air channels.<n>This sensorization approach, termed fluidic innervation, transforms the lattice into a tactile sensor by detecting pressure changes within sealed air channels.
• Score: 41.98879562938879
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tactile sensing plays a fundamental role in enabling robots to navigate dynamic and unstructured environments, particularly in applications such as delicate object manipulation, surface exploration, and human-robot interaction. In this paper, we introduce a passive soft robotic fingertip with integrated tactile sensing, fabricated using a 3D-printed elastomer lattice with embedded air channels. This sensorization approach, termed fluidic innervation, transforms the lattice into a tactile sensor by detecting pressure changes within sealed air channels, providing a simple yet robust solution to tactile sensing in robotics. Unlike conventional methods that rely on complex materials or designs, fluidic innervation offers a simple, scalable, single-material fabrication process. We characterize the sensors' response, develop a geometric model to estimate tip displacement, and train a neural network to accurately predict contact location and contact force. Additionally, we integrate the fingertip with an admittance controller to emulate spring-like behavior, demonstrate its capability for environment exploration through tactile feedback, and validate its durability under high impact and cyclic loading conditions. This tactile sensing technique offers advantages in terms of simplicity, adaptability, and durability and opens up new opportunities for versatile robotic manipulation.

Related papers

• AdaDexGrasp: Adaptive Dexterous Grasping via 3D Visuo-Tactile Representation Fusion [60.79507611737292]
  Humans achieve stable and adaptive grasps by seamlessly integrating visual perception and tactile feedback.<n>Existing robotic grasping approaches rely on visual inputs and lack mechanisms for tactile-guided adaptation after contact.<n>We propose a unified visuo-tactile-fusion grasping framework that integrates grasp generation, feasibility prediction, and adaptive refinement.
  arXiv  Detail & Related papers  (2026-08-06T16:29:11Z)
• Towards Artificial Nerves: Biomimetic Optical-Fiber Tactile Sensing for Robots [5.779924847774939]
  OptiTac is a biomimetic tactile sensor that emulates the mechanoreceptor-to-nerve architecture of human touch.<n>This work demonstrates how evolutionary principles from biology can guide the development of artificial nerve systems for robots.
  arXiv  Detail & Related papers  (2026-07-17T08:34:01Z)
• AetheRock: An Arm-Worn Robot Teaching System for Force-Guided Vision-Tactile Learning [56.20593626288444]
  AetheRock is an arm-worn device featuring a modular and easily manufactured visuo-tactile sensor, GelSlim-MiniFab, at the fingertip.<n>We propose ForceVT, a representation learning framework that uses force and vision to guide fidelity-agnostic tactile learning.
  arXiv  Detail & Related papers  (2026-06-08T17:38:57Z)
• Tactile-Proprioceptive Sensor Fusion for Contact Wrench Estimation in Whole-Body Physical Human-Robot Interaction [6.839826083147316]
  This paper presents a tactile-proprioceptive sensor fusion framework for natural physical human-robot interaction.<n>Tactile cues from pneumatic skin pads serve as contact indicators that bypass the ambiguity between frictional residues and applied external forces.<n>We fuse these cues with motor-current-based proprioception to reconstruct multi-axis contact forces on the robot surface.
  arXiv  Detail & Related papers  (2026-05-27T12:47:25Z)
• Printed helicoids with embedded air channels make sensorized segments for soft continuum robots [40.89188977843952]
  We introduce a fabrication method for embedding air channels into helicoid-based soft continuum robots.<n>We characterize the mechanical properties of four helicoid designs and validate the sensor response to fundamental deformation modes.<n>This approach establishes a scalable fabrication strategy for sensorized architected materials in large-scale soft robotic systems.
  arXiv  Detail & Related papers  (2026-02-26T19:21:29Z)
• UniForce: A Unified Latent Force Model for Robot Manipulation with Diverse Tactile Sensors [51.88112610411651]
  We propose a novel unified tactile representation learning framework that learns a shared latent force space across diverse tactile sensors.<n>UniForce reduces cross-sensor domain shift by jointly modeling inverse dynamics (image-to-force) and forward dynamics (force-to-image)<n>To avoid reliance on expensive external force/torque (F/T) sensors, we exploit static equilibrium and collect force-paired data via direct sensor--object--sensor interactions.
  arXiv  Detail & Related papers  (2026-02-01T11:03:01Z)
• Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulation [50.34179054785646]
  We present Taccel, a high-performance simulation platform that integrates IPC and ABD to model robots, tactile sensors, and objects with both accuracy and unprecedented speed.<n>Taccel provides precise physics simulation and realistic tactile signals while supporting flexible robot-sensor configurations through user-friendly APIs.<n>These capabilities position Taccel as a powerful tool for scaling up tactile robotics research and development.
  arXiv  Detail & Related papers  (2025-04-17T12:57:11Z)
• Digitizing Touch with an Artificial Multimodal Fingertip [51.7029315337739]
  Humans and robots both benefit from using touch to perceive and interact with the surrounding environment. Here, we describe several conceptual and technological innovations to improve the digitization of touch. These advances are embodied in an artificial finger-shaped sensor with advanced sensing capabilities.
  arXiv  Detail & Related papers  (2024-11-04T18:38:50Z)
• A Soft e-Textile Sensor for Enhanced Deep Learning-based Shape Sensing of Soft Continuum Robots [0.3495246564946556]
  The safety and accuracy of robotic navigation hold paramount importance, especially in the realm of soft continuum robotics. Traditional rigid sensors often fail to integrate well with the flexible nature of these robots, adding unwanted bulk and rigidity. This study presents a new approach to shape sensing in soft continuum robots through the use of soft e-textile resistive sensors.
  arXiv  Detail & Related papers  (2024-04-19T05:00:25Z)
• Recognizing Complex Gestures on Minimalistic Knitted Sensors: Toward Real-World Interactive Systems [0.13048920509133805]
  Our digitally-knitted capacitive active sensors can be manufactured at scale with little human intervention. This work advances the capabilities of such sensors by creating the foundation for an interactive gesture recognition system.
  arXiv  Detail & Related papers  (2023-03-18T04:57:46Z)
• Tactile-Filter: Interactive Tactile Perception for Part Mating [54.46221808805662]
  Humans rely on touch and tactile sensing for a lot of dexterous manipulation tasks. vision-based tactile sensors are being widely used for various robotic perception and control tasks. We present a method for interactive perception using vision-based tactile sensors for a part mating task.
  arXiv  Detail & Related papers  (2023-03-10T16:27:37Z)
• Learning to Detect Slip with Barometric Tactile Sensors and a Temporal Convolutional Neural Network [7.346580429118843]
  We present a learning-based method to detect slip using barometric tactile sensors. We train a temporal convolution neural network to detect slip, achieving high detection accuracies. We argue that barometric tactile sensing technology, combined with data-driven learning, is suitable for many manipulation tasks such as slip compensation.
  arXiv  Detail & Related papers  (2022-02-19T08:21:56Z)
• Elastic Tactile Simulation Towards Tactile-Visual Perception [58.44106915440858]
  We propose Elastic Interaction of Particles (EIP) for tactile simulation. EIP models the tactile sensor as a group of coordinated particles, and the elastic property is applied to regulate the deformation of particles during contact. We further propose a tactile-visual perception network that enables information fusion between tactile data and visual images.
  arXiv  Detail & Related papers  (2021-08-11T03:49:59Z)
• Elastic Interaction of Particles for Robotic Tactile Simulation [43.933808122317274]
  We propose Elastic Interaction of Particles (EIP), a novel framework for tactile emulation. EIP models the tactile sensor as a group of coordinated particles, and the elastic theory is applied to regulate the deformation of particles during the contact process. Experiments to verify the effectiveness of our method have been carried out on two applications.
  arXiv  Detail & Related papers  (2020-11-23T16:37:00Z)
• DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation [16.54834671357377]
  General purpose in-hand manipulation remains one of the unsolved challenges of robotics. We introduce DIGIT, an inexpensive, compact, and high-resolution tactile sensor geared towards in-hand manipulation.
  arXiv  Detail & Related papers  (2020-05-29T17:07:54Z)
• OmniTact: A Multi-Directional High Resolution Touch Sensor [109.28703530853542]
  Existing tactile sensors are either flat, have small sensitive fields or only provide low-resolution signals. We introduce OmniTact, a multi-directional high-resolution tactile sensor. We evaluate the capabilities of OmniTact on a challenging robotic control task.
  arXiv  Detail & Related papers  (2020-03-16T01:31:29Z)

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.