arXiv:2606. 14344v1 Announce Type: new Abstract: Tactile imaging seeks to reconstruct the internal structure of soft objects through touch sensing, with applications in medical diagnosis and robotic manipulation.
arXiv:2609.14783v1 Announce Type: cross Abstract: Robots need touch to manipulate objects safely and reliably, as many properties, such as softness, texture, and contact stability, are hard to infer...
arXiv:2606. 24712v1 Announce Type: cross Abstract: Humans effortlessly locate and identify objects by touch alone, even without vision.
Tactile-JEPA is a self‑supervised pre‑training method for distributed tactile sensors that leverages the sensors’ spatial topology to learn topology‑aware representations. It predicts embeddings of masked sensing elements using a sensor connectivity graph and dual‑scale masking to capture both local contact details and the global tactile surface state. Evaluated on three diverse datasets, it improves force estimation by 6.3 % and in‑hand orientation error by 20.8 % over previous state‑of‑the‑art methods, and yields consistent gains in downstream tasks such as policy learning.
ControlTac is a two‑stage framework that generates realistic tactile images conditioned on a single reference image, contact force, and contact pose. By incorporating these physical priors, it produces realistic samples across different sensors and captures task‑relevant variations. Experiments in object insertion, imitation learning, and object weighting show that datasets augmented with ControlTac consistently improve performance in dynamic real‑world settings.
The paper introduces BIFTA, a Brain‑Inspired Few‑Shot Tactile Adaptation framework that enables a frozen encoder to adapt quickly to an unknown tactile sensor using only a small labeled support set. It preserves pretrained representations via dual‑view statistical memory, builds support‑conditioned spectral graphs to correct sensor‑dependent feature neighborhoods, and employs uncertainty‑gated recurrent propagation to reinforce reliable cross‑query evidence. Benchmarks on three tactile datasets demonstrate that BIFTA dramatically improves adaptation performance, achieving an 87.09% mean Sparsh accuracy on SITR with just 10% labeled data—an increase of 47.22 percentage points over the best prior method.
arXiv:2609.24621v1 Announce Type: cross Abstract: Tactile sensing is increasingly being incorporated into learning-based robotic manipulation, yet many existing approaches rely on spatially distribut...
arXiv:2606. 31451v1 Announce Type: cross Abstract: Unified multimodal models (UMMs) have shown great promise in integrating understanding and generation across diverse modalities.
arXiv:2606. 31694v1 Announce Type: cross Abstract: For robots manipulating open-world objects, tactile representations must generalize to unseen materials.
arXiv:2606. 09451v1 Announce Type: cross Abstract: Humans rely on spatially dense, geometry and force-aware tactile feedback at high temporal resolution for dexterous manipulation.
arXiv:2606. 11767v1 Announce Type: cross Abstract: Blind grasping with a dexterous hand is a crucial manipulation capability.
VT-MUSE is a multimodal unified sequential representation learning framework for visuotactile manipulation. It uses a two‑stage approach: first, modality‑specific encoders are jointly adapted with cross‑modal temporal alignment and masked‑view consistency; second, a conditional variational latent model processes masked visual sequences and full tactile histories, with auxiliary decoders reconstructing recent visual observations and predicting tactile depth changes. The resulting representation is fed into a lightweight Transformer policy via gated cross‑attention, achieving an 11‑percentage‑point improvement over the strongest baseline in simulation and significant gains in real‑world experiments.
arXiv:2603. 13869v2 Announce Type: replace-cross Abstract: Dexterous manipulation enables complex tasks but suffers from self-occlusion, severe depth noise, and depth information loss when manipulating transparent objects.