AnyTouch 2: General Optical Tactile Representation Learning For Dynamic Tactile Perception
Ruoxuan Feng, Yuxuan Zhou, Siyu Mei, Dongzhan Zhou, Pengwei Wang, Shaowei Cui, Bin Fang, Guocai Yao, Di Hu
Abstract
Real-world contact-rich manipulation demands robots to perceive temporal tactile feedback, capture subtle surface deformations, and reason about object properties and force dynamics. Although optical tactile sensors are uniquely capable of providing such rich information, existing tactile datasets and models remain limited. These resources primarily focus on object-level attributes (e.g., material) while largely overlooking fine-grained temporal dynamics. We consider that advancing dynamic tactile perception requires a systematic hierarchy of dynamic perception capabilities to guide both data collection and model design. To address the lack of tactile data with rich dynamic information, we present ToucHD, a large-scale tactile dataset spanning tactile atomic actions, real-world manipulations, and touch-force paired data. Beyond scale, ToucHD establishes a comprehensive dynamic data ecosystem that explicitly supports hierarchical perception capabilities from the data perspective. Building on it, we propose AnyTouch 2, a general tactile representation learning framework for diverse optical tactile sensors that unifies object-level understanding with fine-grained, force-aware dynamic perception. The framework captures both pixel-level and action-specific deformations across frames, while explicitly modeling physical force dynamics, thereby learning multi-level dynamic perception capabilities from the model perspective. We evaluate our model on benchmarks that covers static object properties and dynamic physical attributes, as well as real-world manipulation tasks spanning multiple tiers of dynamic perception capabilities—from basic object-level understanding to force-aware dexterous manipulation. Experimental results demonstrate consistent and strong performance across sensors and tasks, highlighting the framework’s effectiveness as a general dynamic tactile perception model. The code, dataset and model are available at gewu-lab.github.io/AnyTouch2/.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Builds on20
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh et al.ICML 2021 · 47,906 citations
- An Image is Worth 16x16 Words: Transformers for Image Recognition at ScaleAlexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn et al.ICLR 2021 · 21,477 citations
- VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-TrainingZhan Tong, Yibing Song, Jue Wang, Limin WangNeurIPS 2022 · 2,336 citations
- A Touch, Vision, and Language Dataset for Multimodal AlignmentLetian Fu, Gaurav Datta, Huang Huang, William Chung-Ho Panitch et al.ICML 2024 · 89 citations
- FineCLIP: Self-distilled Region-based CLIP for Better Fine-grained UnderstandingDong Jing, Xiaolong He, Yutian Luo, Nanyi Fei et al.NeurIPS 2024 · 70 citations
Related papers
- AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile SensorsRuoxuan Feng, Jiangyu Hu, Wenke Xia, Tianci Gao et al.ICLR 2025 · 1 citation
- Universal Visuo-Tactile Video Understanding for Embodied InteractionYifan Xie, Mingyang Li, Shoujie Li, Xingting Li et al.NeurIPS 2025 · 16 citations
- Dynamic Reconstruction of Hand-Object Interaction with Distributed Force-Aware Contact RepresentationZhenjun Yu, Wenqiang Xu, Pengfei Xie, Yutong Li et al.ICCV 2025 · 2 citations
- Visual-Tactile Sensing for In-Hand Object ReconstructionWenqiang Xu, Zhenjun Yu, Han Xue, Ruolin Ye et al.CVPR 2023
- VinT-6D: A Large-Scale Object-in-hand Dataset from Vision, Touch and ProprioceptionZhaoliang Wan, Yonggen Ling, Senlin Yi, Lu Qi et al.ICML 2024 · 11 citations
