aiqbench analysis
Tactus demonstrates the viability of open-vocabulary object recognition using low-cost resistive pressure arrays rather than expensive optical sensors, challenging current reliance on gel-based tactile imaging. The model achieves performance comparable to supervised closed-set CNNs through small-data training and masked autoencoder pretraining while explicitly warning against common pitfalls like vision co-training that can degrade touch-specific accuracy. This work advances the field by providing an open-weight solution for robotics applications where cost-effective, robust sensing is critical.
Tactus: Open-Vocabulary Object Recognition from Low-Cost Pressure Arrays
arXiv:2608.04043v1 Announce Type: cross 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.
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.
6 pages, 4 figures. Weights: this https URL
Machine Learning (cs.LG); Robotics (cs.RO)
arXiv:2608.04043 [cs.LG]
(or arXiv:2608.04043v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.04043 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 295 words
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