# Connecting Touch and Vision via Cross-Modal Prediction

arXiv: https://arxiv.org/abs/1906.06322 · HTML: https://mlsanity.com/paper/1906.06322/

## Key facts

- Cited by 66 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2019.

## Built on by (most cited first, 30 of 66)

- [Binding Touch to Everything: Learning Unified Multimodal Tactile Representations](https://mlsanity.com/paper/2401.18084/) (2024)
- [Touch and Go: Learning from Human-Collected Vision and Touch](https://mlsanity.com/paper/2211.12498/) (2022)
- [ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfer](https://mlsanity.com/paper/2204.02389/) (2022)
- [A Touch, Vision, and Language Dataset for Multimodal Alignment](https://mlsanity.com/paper/2402.13232/) (2024)
- [See, Hear, and Feel: Smart Sensory Fusion for Robotic Manipulation](https://mlsanity.com/paper/2212.03858/) (2022)
- [AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors](https://mlsanity.com/paper/2502.12191/) (2025)
- [Visuo-Tactile Transformers for Manipulation](https://mlsanity.com/paper/2210.00121/) (2022)
- [Transferable Tactile Transformers for Representation Learning Across Diverse Sensors and Tasks](https://mlsanity.com/paper/2406.13640/) (2024)
- [Beyond Sight: Finetuning Generalist Robot Policies with Heterogeneous Sensors via Language Grounding](https://mlsanity.com/paper/2501.04693/) (2025)
- [The ObjectFolder Benchmark: Multisensory Learning with Neural and Real Objects](https://mlsanity.com/paper/2306.00956/) (2023)
- [Multimodal Fusion and Vision-Language Models: A Survey for Robot Vision](https://mlsanity.com/paper/2504.02477/) (2025)
- [Multimodal Visual-Tactile Representation Learning through Self-Supervised Contrastive Pre-Training](https://mlsanity.com/paper/2401.12024/) (2024)
- [Touch in the Wild: Learning Fine-Grained Manipulation with a Portable Visuo-Tactile Gripper](https://mlsanity.com/paper/2507.15062/) (2025)
- [Generating Visual Scenes from Touch](https://mlsanity.com/paper/2309.15117/) (2023)
- [Tactile-Augmented Radiance Fields](https://mlsanity.com/paper/2405.04534/) (2024)
- [Play it by Ear: Learning Skills amidst Occlusion through Audio-Visual Imitation Learning](https://mlsanity.com/paper/2205.14850/) (2022)
- [ARMANI: Part-level Garment-Text Alignment for Unified Cross-Modal Fashion Design](https://mlsanity.com/paper/2208.05621/) (2022)
- [Learning Visuo-Haptic Skewering Strategies for Robot-Assisted Feeding](https://mlsanity.com/paper/2211.14648/) (2022)
- [OPENTOUCH: Bringing Full-Hand Touch to Real-World Interaction](https://mlsanity.com/paper/2512.16842/) (2025)
- [A Survey on Imitation Learning for Contact-Rich Tasks in Robotics](https://mlsanity.com/paper/2506.13498/) (2025)
- [Learning Precise, Contact-Rich Manipulation through Uncalibrated Tactile Skins](https://mlsanity.com/paper/2410.17246/) (2024)
- [AnyTouch 2: General Optical Tactile Representation Learning For Dynamic Tactile Perception](https://mlsanity.com/paper/2602.09617/) (2026)
- [Semi-supervised Multimodal Representation Learning through a Global Workspace](https://mlsanity.com/paper/2306.15711/) (2023)
- [TextToucher: Fine-Grained Text-to-Touch Generation](https://mlsanity.com/paper/2409.05427/) (2024)
- [Dynamic Modeling of Hand-Object Interactions via Tactile Sensing](https://mlsanity.com/paper/2109.04378/) (2021)
- [Controllable Visual-Tactile Synthesis](https://mlsanity.com/paper/2305.03051/) (2023)
- [Self-supervised Hypergraphs for Learning Multiple World Interpretations](https://mlsanity.com/paper/2308.07615/) (2023)
- [M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic Manipulation](https://mlsanity.com/paper/2401.17032/) (2024)
- [Sensor-Invariant Tactile Representation](https://mlsanity.com/paper/2502.19638/) (2025)
- [Touch-based Curiosity for Sparse-Reward Tasks](https://mlsanity.com/paper/2104.00442/) (2021)

## Abstract (arXiv)

Humans perceive the world using multi-modal sensory inputs such as vision, audition, and touch. In this work, we investigate the cross-modal connection between vision and touch. The main challenge in this cross-domain modeling task lies in the significant scale discrepancy between the two: while our eyes perceive an entire visual scene at once, humans can only feel a small region of an object at any given moment. To connect vision and touch, we introduce new tasks of synthesizing plausible tactile signals from visual inputs as well as imagining how we interact with objects given tactile data as input. To accomplish our goals, we first equip robots with both visual and tactile sensors and collect a large-scale dataset of corresponding vision and tactile image sequences. To close the scale gap, we present a new conditional adversarial model that incorporates the scale and location information of the touch. Human perceptual studies demonstrate that our model can produce realistic visual images from tactile data and vice versa. Finally, we present both qualitative and quantitative experimental results regarding different system designs, as well as visualizing the learned representations of our model.