ICRA 2026 Workshop on "7th ViTac Workshop: Learning to See and Feel: Vision–Tactile Synergy for Embodied AI"

Introduction Tactile robotics has emerged as a key frontier of embodied intelligence, equipping robots with the physical sense required to perceive and interact in unstructured environments. By integrating visual and tactile sensing, robots are empowered to perform tasks demanding dexterity, adaptability, and a deep understanding of physical interaction. Recently, the field has witnessed a paradigm shift driven by novel hardware design, high-fidelity simulation and vision-language-action models. The co-design of hardware and algorithm greatly enhances robotic tactile intelligence, while advanced simulators capable of rendering realistic visual-tactile feedback are now bridging the sim-to-real gap, greatly accelerating robot skill learning. The emergence of visual-tactile-language-action models is revolutionizing robot grasping and contact-rich manipulation toward human-level capabilities.

This workshop brings together a diverse group of leading experts and early-career researchers to discuss these important topics. By synthesizing these perspectives, we aim to cover recent advancements in the area, identify remaining scientific gaps, and foster new collaborations toward human-level visual-tactile intelligence.

The workshop will be divided into two sessions, with invited talks from leading experts in academia and industry, poster presentations from young researchers, and a panel discussion.

• Hardware Intelligence and Tactile Simulation
• Robot Dexterity with Visuo-Tactile Fusion: Visual-Tactile Foundation Models

Sessions will be followed by breaks for refreshments, posters, and live demonstrations to encourage interaction among attendees.

Date: Half-day workshop, 8:50 am - 12:30 pm, June 1, 2026
Location: Room Name: Schubert 1, ICRA2026 Venue, Vienna, Austria

Past Workshops:
ViTac2025, ViTac2024, ViTac2023, ViTac2021, ViTac2020, ViTac2019

Invited Speakers

Katherine J. Kuchenbecker
Director, Max Planck Institute for Intelligent Systems

Matei Ciocarlie
Associate Professor, Columbia University

Yan Wu
Principal Scientist, A*STAR Institute for Infocomm Research

Ireti Akinola
Senior Research Scientist, NVIDIA

Maria Bauza Villalonga
Research Scientist, Google DeepMind

Tentative Schedule

Discussion Topics

1. Co-design of Hardware and Algorithms

• Tactile Sensor Design How can we co-design hardware and algorithms to maximize the performance of tactile sensing systems?
• Bioinspired Approaches How can we get inspiration from biological systems to design more effective tactile sensors?
• Scalable Tactile Data Collection Systems How can we build scalable systems for collecting tactile data for large models?

2. High-Fidelity Simulation

• Tactile Interpretation: What is the bottleneck for simulation across diverse tactile sensors (vision-based and non-vision-based)? Should simulators render high-fidelity "raw" signals (e.g., RGB images, electrical signal) or interpreted signals (e.g., marker displacement, force vectors, depth maps) to maximize sim-to-real transfer efficiency?
• Deformable Physics: What are the bottlenecks in modeling elastic materials, soft-body contacts, and complex friction phenomena (stick-slip, hysteresis) in real-time?
• Neural Rendering: Can emerging techniques like Gaussian Splatting replace traditional graphical rendering to generate realistic digital twins for visual-tactile sensing?

3. Sim-to-Real Transfer

• The Reality Gap: What specific physical discrepancies (e.g., visual-tactile signal or physics) are the primary blockers preventing zero-shot transfer of tactile policies?
• Cross-Sensor Transfer: How can we transfer learned skills between tactile sensors with different sensing principles (e.g., optical vs. magnetic) without retraining from scratch?
• Benchmarking Standards: What defines a visual-tactile manipulation policy benchmark? What are the essential datasets, tasks, and evaluation metrics for a benchmark in visual-tactile sensing across simulation and real world?

4. Multimodal Perception & Representation Learning

• Unified Representations across Vision and Touch: What is the unified representation across tactile sensors? How to fusing high-dimensional visual data with sparse, local tactile contact information?
• Dynamic Reweighting for visual-tactile fusion: How should a robot dynamically attribute attention between vision and touch in challenging scenarios (e.g., heavy occlusion, poor lighting)?

5. Control, Learning & Dexterity in the Era of Large Models

• Vision-Tactile-Language-Action Model: How can we effectively integrate visual, tactile, and language information in a large model for robot manipulation?
• Hybrid Architectures: How can we effectively synergize model-based control (for stability and safety) with learning-based paradigms (for adaptability in unstructured environments)?
• Human-Like Reflexes: How do we design control frameworks that decouple high-frequency tactile reflexes (spinal level) from low-frequency visual planning (cortical level)?
• Safety & Robustness: In end-to-end learning frameworks, how do we impose hard safety constraints (e.g., "do not crush") when tactile feedback is noisy or ambiguous?
• Scalability: How can we scale learned policies with visual-tactile sensing across embodiments (sensors and robots)?

Call for Papers/Demos

Topics of Interest

• Development of tactile sensors and visual-tactile data collection systems for robot tasks
• High-fidelity simulation and neural rendering for visual-tactile sensing
• Sim-to-Real for visual-tactile policy learning
• Benchmarking standards, datasets, and evaluation metrics for visual-tactile skill learning
• Visual-tactile foundation models in robot grasping and manipulation
• Visual-tactile representations learning across sensors and embodiments
• Hybrid visual-tactile control architectures combining model-based and learning-based paradigms
• Cognitive control of movement and sensory-motor representations with vision and touch
• The control and design of robotic visuo-tactile systems for human-robot interaction

Posters and live demonstrations will be selected from a call for extended abstracts (2 pages + references), reviewed by the organisers. The best posters will be invited to give talks at the workshop. All submissions will be reviewed using a double-blind review process. Accepted contributions will be presented during the workshop as posters. Submissions must be sent in PDF, following the ICRA conference style (two-columns).

Note on OpenReview Profiles: New profiles created without an institutional email will go through a moderation process that can take up to two weeks. New profiles created with an institutional email will be activated automatically.

• Submission Portal: OpenReview
• Submission Deadline: Apr 10 2026 11:59PM UTC-0
• Notification of acceptance: April 26, 2026
• Camera-ready deadline: May 8, 2026
• Demo Video: Please upload video demos as a single zip under the supplementary material field in OpenReview.
• Dual Submission: Papers to be submitted or in preparation for submission to other major venues in the field are allowed. We also welcome published works as long as explicitly stated at the time of submission.
• Visibility: Submissions and reviews will not be public. Only accepted papers will be made public.
• Contact Email: zhuo.7.chen@kcl.ac.uk

Organisers

Early-Career Organizers

Zhuo Chen
King's College London

Hung-Jui Huang
Carnegie Mellon University

Ruihan Gao
Carnegie Mellon University

Yijiong Lin
University of Bristol

Quan Khanh Luu
Purdue University

Haozhi Qi
Amazon FAR & UChicago

Guadarrama Olvera J. Rogelio
Technical University of Munich

Abu Bakar Dawood
Queen Mary University of London

Senior Advisory Board

Shan Luo
King's College London

Nathan Lepora
University of Bristol

Wenzhen Yuan
University of Illinois Urbana-Champaign

Yu She
Purdue University

Kaspar Althoefer
Queen Mary University of London

Gordon Cheng
Technische Universität München

Sponsors

Platinum Sponsors

Gold Sponsors

Accepted papers

Best Paper winners

1. Tactile Hardware / Perception - Eletac (EleTac: Pneumatic Elephant Trunk-Inspired Soft Gripper with Vision-Based Tactile Sensing)
2. Tactile Simulation / Sim2Real - UniVTAC (UniVTAC: A Unified Simulation Platform for Visuo-Tactile Manipulation Data Generation, Learning, and Benchmarking)
3. Tactile Learning / Representation - GenForce (Training Tactile Sensors to Learn Force Sensing from Each Other)

All Accepted Papers

1. EleTac: Pneumatic Elephant Trunk-Inspired Soft Gripper with Vision-Based Tactile Sensing
   Tuan Tai Nguyen, Xuyang Zhang, Quan Khanh Luu, Shan Luo, Van Ho
   Paper | Poster
2. UniVTAC: A Unified Simulation Platform for Visuo-Tactile Manipulation Data Generation, Learning, and Benchmarking
   Baijun Chen, Weijie Wan, Tianxing Chen, Xianda Guo, Congsheng Xu, Yuanyang Qi, Haojie Zhang, Longyan Wu, Tianling Xu, Zixuan Li, Yizhe Wu, Rui Li, Xiaokang Yang, Ping Luo, Wei Sui, Yao Mu
   Paper | Poster
3. ViTac-Tracing: Visual-Tactile Imitation Learning of Deformable Object Tracing
   Yongqiang Zhao, Shan Luo
   Paper | Poster
4. ViTacGen: Robotic Pushing with Vision-to-Touch Generation
   Zhiyuan Wu, Shan Luo
   Paper | Poster
5. Learning Heterogeneous Tactile Representations with Graph Neural Networks for Dexterous Manipulation
   Tai Yamada, Satoshi Funabashi, Steven Oh, Pranav Ponnivalavan, Kazutaka Omori, Tetsuya Ogata, Shigeki SUGANO
   Paper | Poster
6. Training Tactile Sensors to Learn Force Sensing from Each Other
   Zhuo Chen, Nathan Lepora, Lorenzo Jamone, Jiankang Deng, Shan Luo
   Paper | Poster
7. Development of a High-Speed Event Vision-Based Roller Tactile Sensor for Large-Surface Inspection
   Akram Khairi, Hussain Sajwani, Yahya Zweiri
   Paper | Poster
8. Low-Cost Gating-Based Vision and Proprioception Fusion for Object Property Classification
   Jiaming Zhu, Kaitao Meng
   Paper | Poster
9. Nerves of Plastic: A Transparent Approach To Distributed Tactile Sensing for Safer Robots
   Laura E. Butcher, Christopher J. Ford, Nathan F. Lepora, Efi Psomopoulou
   Paper | Poster
10. One-touch friction estimation at the onset of grasp
    Giuseppe Vitrani, Laurence Willemet, Michael Wiertlewski
    Paper | Poster
11. Grip force regulation via low-dimensional incipient slip estimation
    Laurence Willemet, Giuseppe Vitrani, Michael Wiertlewski
    Paper | Poster
12. Characterisation of a Monolithic 3D-printed Tactile Sensor Using an SSIM-based Analysis
    XIAOQING GUO, Nathan F. Lepora, Efi Psomopoulou
    Paper | Poster
13. GelSLAM: A Real-time, High-Fidelity, and Robust 3D Tactile SLAM System
    Hung-Jui Huang, Mohammad Amin Mirzaee, Michael Kaess, Wenzhen Yuan
    Paper | Poster
14. 3D deformable surface reconstruction from visual and tactile input with geometric prior
    Ioan Laurentiu Popa, Tudor Brezae, Paul-Stelian Sucală, Konievic Robert-Anton, Levente Tamas
    Paper | Poster
15. Real-Time Simulation of Deformable Tactile Sensors and Objects in Robotic Grasping using Graph Neural Networks with Inductive Biases
    Guillaume Duret, Frederik Heller, Danylo Mazurak, Tim Schneider, Alap Kshirsagar, Florence Zara, Jan Peters, Liming Chen
    Paper | Poster
16. Automatic Physically-Based Sim2Real for Tactile Images through Differentiable Path-Tracing Rendering
    Guillaume Duret, Anna Samsonenko, Florence Zara, Jan Peters, Liming Chen
    Paper | Poster
17. roto 2.0: The Robot Tactile Olympiad
    Elle Miller, Jayaram Reddy, Ayush Deshmukh, Trevor McInroe, David Abel, Oisin Mac Aodha, Sethu Vijayakumar
    Paper | Poster
18. TactileLab: Efficient Shear-Sensitive Tactile Simulation for Dexterous Sim2Real Robotic Manipulation
    Yijiong Lin, Nathan F. Lepora
    Paper | Poster
19. NeuralTouch: Leveraging Implicit Neural Descriptor for Precise Sim-to-Real Tactile Robot Control
    Yijiong Lin, chenghua lu, Max Yang, Efi Psomopoulou, Nathan F. Lepora
    Paper | Poster
20. A Robust Shear-Based Optical Tactile Sensor for Quadruped-Mounted Manipulation
    Assylkhan Seitzhanov, Jabrail Chumakov, Zhanat Kappassov
    Paper