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...
By Mashood M. Mohsan, Muhayy Ud Din, Binzhao Xu, Ahmad Abubakar, Irfan Hussain
arXiv:2606. 24712v1 Announce Type: cross Abstract: Humans effortlessly locate and identify objects by touch alone, even without vision.
By Shivani Kamtikar, Chung Hee Kim, Camilla Tabasso, Tye Brady, Joshua Migdal, Taskin Padir
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.
By Elizaveta Kovtun, Matvey Konovalov, Andrey Sakhovskiy, Semen Budennyy
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.
By Dongyu Luo, Kelin Yu, Amir-Hossein Shahidzadeh, Cornelia Ferm\"uller, Yiannis Aloimonos, Ruohan Gao
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.
By Boheng Liu, Ziyu Li, Xia Wu
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...
By Joseph Rigal, Emmanuel Virot, Caroline Pascal