Vibro-Sense: Robust Vibration-based Impulse Response Localization and Trajectory Tracking for Robotic Hands
- URL: http://arxiv.org/abs/2601.20555v1
- Date: Wed, 28 Jan 2026 12:49:39 GMT
- Title: Vibro-Sense: Robust Vibration-based Impulse Response Localization and Trajectory Tracking for Robotic Hands
- Abstract summary: Rich contact perception is crucial for robotic manipulation, yet traditional tactile skins remain expensive and complex to integrate.<n>This paper presents a scalable alternative: high-accuracy whole-body touch localization via vibro-acoustic sensing.
- Score: 0.5500249707065662
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Rich contact perception is crucial for robotic manipulation, yet traditional tactile skins remain expensive and complex to integrate. This paper presents a scalable alternative: high-accuracy whole-body touch localization via vibro-acoustic sensing. By equipping a robotic hand with seven low-cost piezoelectric microphones and leveraging an Audio Spectrogram Transformer, we decode the vibrational signatures generated during physical interaction. Extensive evaluation across stationary and dynamic tasks reveals a localization error of under 5 mm in static conditions. Furthermore, our analysis highlights the distinct influence of material properties: stiff materials (e.g., metal) excel in impulse response localization due to sharp, high-bandwidth responses, whereas textured materials (e.g., wood) provide superior friction-based features for trajectory tracking. The system demonstrates robustness to the robot's own motion, maintaining effective tracking even during active operation. Our primary contribution is demonstrating that complex physical contact dynamics can be effectively decoded from simple vibrational signals, offering a viable pathway to widespread, affordable contact perception in robotics. To accelerate research, we provide our full datasets, models, and experimental setups as open-source resources.
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) - Synthetic-to-Real Translation for Class-Agnostic Motion Prediction [64.55842058684583]
We explore the potential of synthetic-to-real translation for motion prediction (SRMP)<n>We propose a novel approach integrating a motion knowledge translation framework with two key components.<n>We present a physically-based pipeline for generating Motion4D, the first synthetic 4D LiDAR dataset tailored for SRMP research.
arXiv Detail & Related papers (2026-07-07T14:19:22Z) - T-Rex: Tactile-Reactive Dexterous Manipulation [139.71263755530654]
We introduce a variable-rate Mixture-of-Transformers (MoT) architecture equipped with a novel temporal tactile VQ-VAE encoder.<n>We demonstrate the effectiveness of tactile-reactive policies on 12 manipulation tasks requiring delicate force control and deformable object manipulation.
arXiv Detail & Related papers (2026-06-15T17:59:55Z) - Learn Weightlessness: Imitate Non-Self-Stabilizing Motions on Humanoid Robot [56.377161933570335]
Humans naturally exploit a "weightless" state during non-self-stabilizing (NSS) motions.<n>Inspired by this biological mechanism, we propose a weightlessness-state auto-labeling strategy for dataset annotation.<n>Our work bridges the gap between precise trajectory tracking and adaptive environmental interaction, offering a biologically-inspired solution for contact-rich humanoid control.
arXiv Detail & Related papers (2026-04-23T07:10:05Z) - A Dynamic Toolkit for Transmission Characteristics of Precision Reducers with Explicit Contact Geometry [5.7347536097223335]
Precision reducers are critical components in robotic systems, directly affecting the motion accuracy and dynamic performance of humanoid robots, quadruped robots, collaborative robots, industrial robots, and SCARA robots.<n>This paper presents a dynamic toolkit for analyzing the transmission characteristics of precision reducers with explicit contact geometry.<n>A unified framework is proposed to address the challenges in modeling accurate contact behaviors, evaluating gear stiffness, and predicting system vibrations.
arXiv Detail & Related papers (2026-04-02T03:29:01Z) - A Robotic Testing Platform for Pipelined Discovery of Resilient Soft Actuators [63.07587517846448]
We propose an optimization pipeline enabled by a novel testing robot capable of scanning DEA lifetime.<n>The robot integrates electro-mechanical property measurement, programmable voltage input, and multi-channel testing capacity.<n>Using it, we scanned the lifetime of Elastosil-based linear actuators across parameters including input voltage magnitude, frequency, electrode material concentration, and electrical connection filler.
arXiv Detail & Related papers (2026-02-24T14:41:28Z) - Sound of Touch: Active Acoustic Tactile Sensing via String Vibrations [10.834682105378269]
Sound of Touch is an active acoustic tactile-sensing methodology that uses vibrating tensioned strings as sensing elements.<n>We derive a physics-based string-vibration simulator that predicts how contact position and force shift vibration modes.<n>Experiments demonstrate millimeter-scale localization, reliable force estimation, and real-time slip detection.
arXiv Detail & Related papers (2026-02-18T20:18:40Z) - MeshMimic: Geometry-Aware Humanoid Motion Learning through 3D Scene Reconstruction [54.36564144414704]
MeshMimic is an innovative framework that bridges 3D scene reconstruction and embodied intelligence to enable humanoid robots to learn coupled "motion-terrain" interactions directly from video.<n>By leveraging state-of-the-art 3D vision models, our framework precisely segments and reconstructs both human trajectories and the underlying 3D geometry of terrains and objects.
arXiv Detail & Related papers (2026-02-17T17:09:45Z) - 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) - Fluidically Innervated Lattices Make Versatile and Durable Tactile Sensors [43.33741954317892]
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.
arXiv Detail & Related papers (2025-07-28T18:00:04Z) - Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulation [34.47272224723296]
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>Unlike previous simulators that operate at sub-real-time speeds with limited parallelization, Taccel provides precise physics simulation and realistic tactile signals.<n>These capabilities position Taccel as a powerful tool for scaling up tactile robotics research and development, potentially transforming how robots interact with and understand their physical environment.
arXiv Detail & Related papers (2025-04-17T12:57:11Z) - Multimodal Anomaly Detection based on Deep Auto-Encoder for Object Slip
Perception of Mobile Manipulation Robots [22.63980025871784]
The proposed framework integrates heterogeneous data streams collected from various robot sensors, including RGB and depth cameras, a microphone, and a force-torque sensor.
The integrated data is used to train a deep autoencoder to construct latent representations of the multisensory data that indicate the normal status.
Anomalies can then be identified by error scores measured by the difference between the trained encoder's latent values and the latent values of reconstructed input data.
arXiv Detail & Related papers (2024-03-06T09:15:53Z) - 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)
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.