MultiDiffSense: Diffusion-Based Multi-Modal Visuo-Tactile Image Generation Conditioned on Object Shape and Contact Pose
Department of Bioengineering, Imperial-X Initiative, Imperial College London（生物工程系、Imperial-X计划、帝国理工学院伦敦分校）；CAMS-Oxford Institute, University of Oxford（CAMS-牛津研究所、牛津大学）
Sirine Bhouri, Lan Wei, Jian-Qing Zheng, Dandan Zhang
Acquiring aligned visuo-tactile datasets is slow and costly, requiring specialised hardware and large-scale data collection. Synthetic generation is promising, but prior methods are typically single-modality, limiting cross-modal learning. We present MultiDiffSense, a unified diffusion model that synthesises images for multiple vision-based tactile sensors (ViTac, TacTip, ViTacTip) within a single architecture. Our approach uses dual conditioning on CAD-derived, pose-aligned depth maps and structured prompts that encode sensor type and 4-DoF contact pose, enabling controllable, physically consistent multi-modal synthesis. Evaluating on 8 objects (5 seen, 3 novel) and unseen poses, MultiDiffSense outperforms a Pix2Pix cGAN baseline in SSIM by +36.3% (ViTac), +134.6% (ViTacTip), and +64.7% (TacTip). For downstream 3-DoF pose estimation, mixing 50% synthetic with 50% real halves the required real data while maintaining competitive performance. MultiDiffSense alleviates the data-collection bottleneck in tactile sensing and enables scalable, controllable multi-modal dataset generation for robotic applications.
SoftVTBench: A Safety-Aware Visuo-Tactile Benchmark for Physically Constrained Robotic Manipulation of Deformable Objects (Early Version)
Tuojing Intelligence（拓景智能）；Tsinghua University（清华大学）；King’s College London（伦敦国王学院）；Southeast University（东南大学）；Stevens Institute of Technology（史蒂文斯理工学院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Manchester（曼彻斯特大学）；Simple AI（简单人工智能公司）；Imperial College London（伦敦帝国学院）；Carnegie Mellon University（卡内基梅隆大学）；Zhejiang University（浙江大学）；Beihang University（北京航空航天大学）；The University of Hong Kong（香港大学）
Seeing Through Touch: Tactile-Driven Visual Localization of Material Regions
Korea Advanced Institute of Science and Technology（韩国科学技术院）；Hankuk University of Foreign Studies（韩国外国语大学）；Ulsan National Institute of Science and Technology（蔚山科学技术院）
A Transfer Learning Approach to Cross-Modal Object Recognition: From Visual Observation to Robotic Haptic Exploration
Technical University of Munich（慕尼黑工业大学）；ABB Corporate Research（ABB集团研究院）；University of Campania Luigi Vanvitelli（坎帕尼亚大学路易吉·万维泰利）；Institute of Robotics and Mechatronics, German Aerospace Center（德国航空航天中心机器人与机电一体化研究所）
ViTaPEs: Visuotactile Position Encodings for Cross-Modal Alignment in Multimodal Transformers
Chair of Cyber-Physical Systems（感知系统教授席位）；Institute of Machine Learning and Neural Computation（机器学习与神经计算研究所）；Technical University of Leoben（莱比锡技术大学）；Graz University of Technology（格拉茨技术大学）
