Robbyant/lingbot-touch

Bing Han, Yichuan Peng, Haoyu Dong, Yuanmeng Hu, Yongyi Jia, Fangjing Wang, Kecheng Zheng, Yiming Zhang, Yanhong Zeng, Xiaoqing Zhang, Chen Song, Rundong Zou, Anqi Dong, Yuan Guan, Ka Leong Cheng, Xing Zhu, Yujun Shen, Nan Xue, Wei Ning† We aim to bridge heterogeneous tactile sensor signals and real-world dexterous robotic manipulation, enabling tactile experience to transfer across hardware platforms and tasks. To this end, we introduce LingBot-Touch, a JEPA-based tactile representation model built around a shared Transformer and latent representation prediction from spatiotemporal tactile context. LingBot-Touch enables a rich understanding of spatiotemporal tactile signals across sensing modalities while supporting high-frequency inference and cross-task transfer. We demonstrate four key properties: LingBot-Touch learns unified representations of magnetic, piezoresistive, and visuotactile signals through masked prediction in latent space, using modality-specific tokenizers and a shared Transformer. See the paper for method details. Inference release. This repository provides the frozen target encoder and utilities…