Tactus: Open-Vocabulary Object Recognition from Low-Cost Pressure Arrays

• URL: http://arxiv.org/abs/2608.04043v1
• Date: Tue, 04 Aug 2026 06:08:21 GMT
• Title: Tactus: Open-Vocabulary Object Recognition from Low-Cost Pressure Arrays
• Abstract summary: We present Tactus, an open model that answers text queries from pressure data alone.<n>The recipe is small-data: 187 training recordings, masked-autoencoder pretraining on 144k unlabeled same-sensor frames, and the sensor's own calibration affine.<n>Weights, code, and the memory layer the model plugs into are released openly.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Resistive pressure arrays are the cheapest and most widely shipped tactile sensors, yet tactile representation learning has concentrated on optical sensors that image a deforming gel. We present Tactus, an open model that answers text queries from pressure data alone: on the STAG benchmark (27 objects, held-out recordings), it reaches 0.771 +/- 0.062 top-1 over four runs (top-3 0.935), matching, and at best exceeding, the dataset's supervised closed-set CNN at 0.76, with no trained classifier head. The recipe is small-data: 187 training recordings, masked-autoencoder pretraining on 144k unlabeled same-sensor frames, and the sensor's own calibration affine, which recovered more accuracy than every architecture change combined. The released model's errors concentrate in a few contact-ambiguous classes, are uncorrelated with text-target geometry (Spearman rho <= 0.05 over 702 class pairs), and survive paraphrased and even bare-name queries within one point; two diverse frames recover 89% of eight-frame accuracy. Failures are reported with equal precision: cross-sensor pretraining pooling gave no gain, vision co-training degraded touch, and a mis-normalized input pipeline silently discarded 97% of the sensor's dynamic range while producing plausible intermediate results. Weights, code, and the memory layer the model plugs into are released openly.

Related papers

• Robust Slip Detection and Material Classification via Spatiotemporal Transformers on a Uniformly-Illuminated Visuo-Tactile Sensor [52.33204893988409]
  We propose a visuo-tactile sensor featuring customized uniform illumination, alongside a unified perception framework.<n>The proposed hardware-software framework provides high-fidelity feedback and a powerful multi-modal perception baseline for complex robotic manipulation.
  arXiv  Detail & Related papers  (2026-08-25T07:25:27Z)
• Self-Calibrating Dense Displacement Fields for Reliable Co-Registration of Large Optical Satellite Imagery [4.730120849611304]
  Co-registration underlies nearly every multi-temporal and multi-sensor use of optical satellite imagery.<n>We present SCDF, a training-free, GPU-free estimator whose motion model is the dense per-pixel displacement field itself.<n>On 584 constructed-ground-truth pairs built from real Sentinel Landsat-8/9, and NAIP imagery, SCDF registers every pair with zero failures.
  arXiv  Detail & Related papers  (2026-08-23T08:50:59Z)
• A Model-Based Decoupling Strategy for Proprioception and Contact Sensing in an Architected Soft Manipulator [49.225033144914214]
  We present a model-based strategy that decouples proprioceptive and contact signals from fluidic pressure sensors embedded in a soft architected segment.<n>On a single ITH segment, this approach achieves proprioceptive shape estimation with a relative bending error of 0.11 +/- 0.02 and a contact detection rate of 97% across 178 trials.
  arXiv  Detail & Related papers  (2026-07-17T03:03:19Z)
• A Masked Autoencoder Approach to Unsupervised Steel Surface Defect Recognition [0.0]
  A Transformer based Masked Autoencoder is used here to learn representations of steel surface defects for unsupervised grouping.<n>The decoder reaches a structural similarity score of 0.92 and a mean squared error of 0.47.
  arXiv  Detail & Related papers  (2026-07-14T18:26:36Z)
• OctoSense: Self-Supervised Learning for Multimodal Robot Perception [50.20346079739425]
  We present OctoSense, an open-source sensor platform with stereo and event cameras, LiDAR, a thermal camera, an inertial measurement unit, and proprioception (CAN bus data from a car, and joint angles for a quadruped robot)<n>The eponymous OctoSense dataset contains 59 hours of time-synchronized driving data across different types of environments at different times of the day.<n>We demonstrate multi-modal self-supervised learning using such real-world data, where sensors have different representations, frequencies, latencies and noise.
  arXiv  Detail & Related papers  (2026-06-25T17:30:49Z)
• TacVerse: A Multi-Sensor Dataset and Benchmark for Cross-Sensor Vision-Based Tactile Perception [7.968185063285978]
  We present TacVerse, a multi-sensor dataset and benchmark for cross-sensor vision-based tactile perception.<n>The dataset contains 106,800 tactile images from seven vision-based tactile sensors.<n>Strong within-sensor performance across all tasks indicates that the collected tactile observations are informative for the target objectives.
  arXiv  Detail & Related papers  (2026-06-24T14:22:48Z)
• iSAGE: A Human-in-the-Loop Framework for Remote Sensing Semantic Segmentation via Sparse Point Supervision [0.1749935196721634]
  Existing human-in-the-loop frameworks expand sparse clicks into dense supervision via auxiliary machinery.<n>This paper hypothesizes that expert clicks targeting confident model errors, not arbitrary pixels, suffice to match dense supervision.<n>iSAGE is the only iterative human-in-the-loop framework operating without auxiliary machinery.
  arXiv  Detail & Related papers  (2026-06-08T20:09:35Z)
• Amplifying, Not Learning: Fine-Tuned AI Text Detectors Amplify a Pretrained Direction [51.56484100374058]
  Text detectors amplify a pretrained typicality axis; they do not construct an AI-vs-human boundary.<n>On raw encoders before any task supervision, projecting onto centroid(AI)-centroid(HC3) achieves NYT-vs-HC3 AUROC 0.806/0.944/0.834 across three architectures.<n>On RoBERTa-base, raw projection exceeds fine-tuning; on RoBERTa-base, full fine-tuning reduces discrimination below raw on both fluent-formal populations tested.
  arXiv  Detail & Related papers  (2026-05-20T19:08:38Z)
• Symbolic Augmentation Closes a Canonical-Equivalence Blind Spot in Neural Fact-Checkers [1.7844382164707184]
  We introduce a five-class typed-quantity error taxonomy and a 1500-item benchmark.<n>A ModernBERT encoder fine-tuned on this benchmark reaches macro-F1 = 0.899, far above any off-the-shelf neural fact-checker.<n>We propose Symbolic Augmentation, a training-time framework that runs the modules of a symbolic verifier in reverse to generate label-preserving augmented training data.
  arXiv  Detail & Related papers  (2026-05-19T15:58:00Z)
• Design and Validation of a Lightweight 1D CNN for Affective Touch Classification in Soft Plush Companions [59.49967357689445]
  Soft, sensorized companions offer a physically safe and emotionally intuitive interface for socially assistive technologies.<n>This study presents a complete open-source dilated real-based framework for the development and validation of deep learning models for affective touch recognition in soft interactive companions.
  arXiv  Detail & Related papers  (2026-04-16T10:20:05Z)
• No Dense Tensors Needed: Fully Sparse Object Detection on Event-Camera Voxel Grids [0.3823356975862005]
  Event cameras produce asynchronous, high-dynamic-range streams well suited for detecting small, fast-moving drones.<n>Most event-based detectors convert the sparse event stream into dense tensors, discarding the representational efficiency of neuromorphic sensing.<n>We propose SparseVoxelDet, the first fully sparse object detector for event cameras, in which backbone feature extraction, feature pyramid fusion, and the detection head all operate exclusively on occupied voxel positions.
  arXiv  Detail & Related papers  (2026-03-23T07:12:14Z)
• Camouflage is all you need: Evaluating and Enhancing Language Model Robustness Against Camouflage Adversarial Attacks [53.87300498478744]
  Adversarial attacks represent a substantial challenge in Natural Language Processing (NLP) This study undertakes a systematic exploration of this challenge in two distinct phases: vulnerability evaluation and resilience enhancement. Results suggest a trade-off between performance and robustness, with some models maintaining similar performance while gaining robustness.
  arXiv  Detail & Related papers  (2024-02-15T10:58:22Z)
• Deep Soft Procrustes for Markerless Volumetric Sensor Alignment [81.13055566952221]
  In this work, we improve markerless data-driven correspondence estimation to achieve more robust multi-sensor spatial alignment. We incorporate geometric constraints in an end-to-end manner into a typical segmentation based model and bridge the intermediate dense classification task with the targeted pose estimation one. Our model is experimentally shown to achieve similar results with marker-based methods and outperform the markerless ones, while also being robust to the pose variations of the calibration structure.
  arXiv  Detail & Related papers  (2020-03-23T10:51:32Z)

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.