Tactile Sensing for Robotic Manipulation

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TACO: TActile World Model as a Self-COrrector for Scalable Robot Policy Post-Training
Jul 3, 2026Shengbang Liu, Yueru Jia, Yuyang Yan +7Robot Skill Learning World Models for Robotics
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University · Sun Yat-sen University · AI2 Robotics +1
Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation
Jul 1, 2026Chi Zhang, Penglin Cai, Ziheng Xi +6Contact-Rich Robotic Manipulation Generalization in Robotic Manipulation
1Peking University · 2BeingBeyond · 3Tsinghua University
TactX: Learning Shared Tactile Representations Across Diverse Sensors
Jun 30, 2026Junsung Park, Sachin Bhadang, Carmelo Sferrazza +2Cross-Modal Representation Learning Contact-Rich Robotic Manipulation
UC San Diego · Seoul National University · Amazon FAR
Heterogeneous Tactile Transformer
Jun 29, 2026Jianxin Bi, Qiang Wang, Jayaram Reddy +4Representation Learning Multimodal Pretraining
National University of Singapore · Carnegie Mellon University · 3Smart Systems Institute, NUS
TacGen: Touch Is a Necessary Dimension of Physical-World Representation -- Addressing Tactile Data Scarcity with Scalable Vision-to-Touch Alignment and Generation
Jun 28, 2026Wanghao Ye, Aarosh Das, Sihan Chen +19Visuo-Tactile Representation Learning Multimodal Contrastive Learning
University of Maryland, College Park · Carnegie Mellon University · University of Alabama +2
TAP-VLA: Tactile Annotation Prompting for Vision Language Action Models
Jun 27, 2026Mark Van der Merwe, Mohamad Louai Shehab, Jayjun Lee +4Vision-Language-Action Models Tactile Sensing for Robotic Manipulation
Robotics Department, University of Michigan · Computer Science and Engineering Department, University of Michigan
You Only Touch Once: 6-DoF Object Pose Estimation from Single Tactile Contact
Jun 27, 2026Pengfei Ye, Yuxiang Ma, Haonan Chen +5Object Pose Estimation Robotic Manipulation
MIT CSAIL · Harvard University · University of Cambridge
VibeAct: Vibration to Actions for Contact-Rich Reactive Robot Dexterity
Jun 25, 2026Yuemin Mao, Uksang Yoo, Jean Oh +2Tactile Sensing Contact-Rich Robotic Manipulation
1Carnegie Mellon University · 2Bosch Center for Artificial Intelligence
DexTeleop-0: Force-Aware Bimanual Dexterous Teleoperation with Ego-Centric Perception towards Shared Autonomy
Jun 22, 2026Haichao Liu, Yuyao Jiang, Hyunsun Park +2Dexterous Manipulation Bimanual Robotic Manipulation
Nanyang Technological University, Singapore · OOJU, USA
Tactile Genesis: Exploring Tactile Sensors at Scale for Learning Dexterous Tasks
Jun 21, 2026Trinity Chung, Kashu Yamazaki, Dhruv Patel +4Dexterous Manipulation Robotics Simulation
1Carnegie Mellon University · 2Genesis AI
HT-Bench: Benchmarking and Learning Dexterous Full-Hand Tactile Representations with Egocentric Vision
Jun 17, 2026Yuzhe Huang, Jiaping Wu, Jiaming Jiang +6Representation Learning Visuo-Tactile Representation Learning
Beihang University · Rimbot · BUPT +3
Contact-Based Fringe Projection Profilometry for High-Resolution 3-D Surface Measurement of Reflective and Transparent Objects
Jun 16, 2026Ingu Yeo, Hyung-Gun Chi, Jae-Sang HyunFringe Projection Profilometry Tactile Sensing
Department of Mechanical Engineering and the Yonsei Institute for Embodied Intelligence, Yonsei University, Seoul 03722, South Korea
T-Rex: Tactile-Reactive Dexterous Manipulation
Jun 15, 2026Dantong Niu, Zhuoyang Liu, Zekai Wang +31Representation Learning Visuo-Tactile Representation Learning
UC Berkeley · NVIDIA · La Sapienza University +3
Inference-time Policy Steering via Vision and Touch
Jun 12, 2026Yilin Wu, Zilin Si, Zeynep Temel +2Robot Policy Learning Robot Policy Adaptation
Carnegie Mellon University
ContactWorld: What Representations Matter for Vision-Tactile Latent World Models in Contact-Rich Manipulation
Jun 11, 2026Zhiyuan Zhang, Pokuang Zhou, Kaidi Zhang +7Representation Learning World Models for Robotics
Department of Industrial Engineering, Purdue University, West Lafayette, IN 47907, USA · Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843, USA
WT-UMI: Tactile-based Whole-Body Manipulation via Force-Supervised Contact-Aware Planning
Jun 11, 2026Jaehwi Jang, Zhaoyuan Gu, Alfred Cueva +15Contact-Rich Robotic Manipulation Whole-Body Robotic Manipulation
The Institute for Robotics and Intelligent Machines, Georgia Institute of Technology
Deformable In-Hand Slip-Aware Tactile Sensor with Integrated Velocity Sensing, Force/Torque and Pressure Map Estimation
Jun 10, 2026Gabriel Arslan Waltersson, Yiannis KarayiannidisTactile Sensing Tactile Sensing for Robotic Manipulation
Department of Electrical Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden · Department of Automatic Control, Lund University, Sweden
Blind Dexterous Grasping via Real2Sim2Real Tactile Policy Learning
Jun 10, 2026Shengcheng Luo, Xiyan Huang, Zhe Xu +3Robotic Grasping Representation Learning
1ShanghaiTech University · 2Beijing Institute for General Artificial Intelligence
TacCoRL: Integrating Tactile Feedback into VLA via Simulation
Jun 10, 2026Siyu Ma, Yuqi Liang, Chang Yu +5RL for Robotics Robot Skill Learning
University of California, Los Angeles · University of California, San Diego · 4Peking University +1
TacForeSight: Force-Guided Tactile World Model for Contact-Rich Manipulation
Jun 9, 2026Yujie Zang, Yuhang Zheng, Xian Nie +7World Models for Robotics Contact-Rich Robotic Manipulation
TARS Robotics · National University of Singapore · Shanghai Jiao Tong University +2
AetheRock: An Arm-Worn Robot Teaching System for Force-Guided Vision-Tactile Learning
Jun 8, 2026Hong Li, Yue Xu, Yihan Tang +8Representation Learning Contact-Rich Robotic Manipulation
Shanghai Jiao Tong University · Beijing Institute for General Artificial Intelligence (BIGAI) · Ant Group +1
TORL-VLA: Tactile Guided Online Reinforcement Learning for Contact-Rich Manipulation
Jun 8, 2026Huaihang Zheng, Yi Yang, Kai Ma +8RL for Robotics Long-Horizon Robotic Manipulation
Meituan · State Key Lab of Multimodal Artificial Intelligence Systems, Institute of Automation, CAS · Beijing Institute of Technology +2
EgoTactile: Learning Grasp Pressure for Everyday Objects from Egocentric Video
Jun 8, 2026Yuan Zeng, Yujia Shi, Tiao Tan +6Vision-Based Tactile Sensing Video Diffusion Models
Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China · Department of Network, Pengcheng Laboratory, Shenzhen, China +2
RGB-S: Image-Aligned Tactile Saliency for Robust Dexterous Manipulation
Jun 7, 2026Shengcheng Luo, Kefei Wu, Xiaoying Zhou +3Contact-Rich Robotic Manipulation Visuo-Tactile Representation Learning
ShanghaiTech University · Beijing Institute for General Artificial Intelligence
Dream-Tac: A Unified Tactile World Action Model for Contact-Rich Robot Manipulation
Jun 7, 2026Yunfan Lou, Yifan Ye, Yankai Fu +7World Models for Robotics Contact-Rich Robotic Manipulation
Peking University Beijing, China · Nanjing University Nanjing, China · State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University Beijing, China
Multi-Resolution Tactile Imitation Learning for Contact-Rich Robotic Manipulation
Jun 4, 2026Rickmer Krohn, Erik Helmut, Niklas Funk +3Tactile Sensing Contact-Rich Robotic Manipulation
Interactive Robot Perception & Learning, TU Darmstadt · Hessian AI · Robotics Institute Germany +1
Learning Controlled Separation of Small Objects Between Two Fingers with a Tactile Skin
May 29, 2026Ulf Kasolowsky, Berthold BäumlTactile Sensing for Robotic Manipulation
Learning AI for Dextrous Robots Lab (aidx-lab.org), Technical University of Munich, Germany · DLR Institute of Robotics and Mechatronics, German Aerospace Center
ARISTO Hand: Sensing-Driven Distal Hyperextension for Fine-Grained Manipulation
May 28, 2026Aaron Kim, Dong Ho Kang, Mark Helwig +4Robotic Hand Design Robotic Grasping
Human Centered Robotics Lab at The University of Texas at Austin, TX, USA. · Sony Group Corporation at Tokyo, Japan.
Beyond Binary: Sim-to-Real Dexterous Manipulation with Physics-Grounded Contact Representation
May 27, 2026Jiahe Pan, Stelian Coros, Jitendra Malik +1RL for Robotics Tactile Sensing
1ETH Zürich · 2UC Berkeley
Tabero: Learning Gentle Manipulation with Closed-Loop Force Feedback from Vision, Touch, and Language
May 27, 2026Qiwei Wu, Rui Zhang, Xin Xiang +4Language-Conditioned Robot Manipulation Robotic Manipulation
Nvidia, Beijing, China · Hong Kong University of Sci

Jul 3, 2026, cs.ROJ/K move · Enter open · S save

TACO: TActile World Model as a Self-COrrector for Scalable Robot Policy Post-Training

Shengbang Liu, Yueru Jia, Yuyang Yan, Jiaming Liu+6

State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University · Sun Yat-sen University · AI2 Robotics+1

Robot Skill Learning World Models for Robotics Contact-Rich Robotic Manipulation Tactile Sensing for Robotic Manipulation Tactile World Models

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Vision-Language-Action models and World Action Models have shown promising generalization in robotic manipulation but remain fragile in contact-rich tasks, where contact perturbations can cause failures that are difficult to detect from vision alone. Corrective post-training with tactile feedback can improve recovery, but scaling such supervision through human intervention is costly. World models can synthesize additional training data, yet vision-only generation may produce visually plausible but contact-inconsistent trajectories. We therefore introduce TACO, a scalable robot policy post-training framework built on a compositional tactile world model. Given real rollouts, TACO follows a Recognize--Imagine--Label loop: an inverse dynamics and value model identifies failure-adjacent states using progress estimates, a visuo-tactile generation model imagines local corrections by jointly generating video and tactile sequences, and the inverse dynamics and value model labels them with corrective actions and progress scores. Candidates are filtered for kinematic feasibility and tactile plausibility, then selected by predicted progress gain. TACO aggregates demonstrations, real rollouts, and selected corrections for iterative post-training. It combines knowledge-insulated tactile adaptation with CFG-RL using binary advantage labels while keeping the pretrained VLM backbone fixed. Experiments on real-world tasks show that TACO improves the average task score from 0.375 to 0.825 after two post-training iterations.

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