# Tactile Gym 2.0: Sim-to-real Deep Reinforcement Learning for Comparing Low-cost High-Resolution Robot Touch

arXiv: https://arxiv.org/abs/2207.10763 · HTML: https://mlsanity.com/paper/2207.10763/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2022.

## Built on by (most cited first, 18 of 18)

- [Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI](https://mlsanity.com/paper/2407.06886/) (2024)
- [Multimodal Visual-Tactile Representation Learning through Self-Supervised Contrastive Pre-Training](https://mlsanity.com/paper/2401.12024/) (2024)
- [The Power of the Senses: Generalizable Manipulation from Vision and Touch through Masked Multimodal Learning](https://mlsanity.com/paper/2311.00924/) (2023)
- [AnyRotate: Gravity-Invariant In-Hand Object Rotation with Sim-to-Real Touch](https://mlsanity.com/paper/2405.07391/) (2024)
- [Learning to Read Braille: Bridging the Tactile Reality Gap with Diffusion Models](https://mlsanity.com/paper/2304.01182/) (2023)
- [TouchSDF: A DeepSDF Approach for 3D Shape Reconstruction using Vision-Based Tactile Sensing](https://mlsanity.com/paper/2311.12602/) (2023)
- [Learning In-Hand Translation Using Tactile Skin With Shear and Normal Force Sensing](https://mlsanity.com/paper/2407.07885/) (2024)
- [Snap-it, Tap-it, Splat-it: Tactile-Informed 3D Gaussian Splatting for Reconstructing Challenging Surfaces](https://mlsanity.com/paper/2403.20275/) (2024)
- [DrS: Learning Reusable Dense Rewards for Multi-Stage Tasks](https://mlsanity.com/paper/2404.16779/) (2024)
- [Multimodal and Force-Matched Imitation Learning with a See-Through Visuotactile Sensor](https://mlsanity.com/paper/2311.01248/) (2023)
- [M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic Manipulation](https://mlsanity.com/paper/2401.17032/) (2024)
- [Data Pyramid for Embodied Manipulation: A Survey](https://mlsanity.com/paper/2607.24744/) (2026)
- [Tactile MNIST: Benchmarking Active Tactile Perception](https://mlsanity.com/paper/2506.06361/) (2025)
- [ViTacGen: Robotic Pushing with Vision-to-Touch Generation](https://mlsanity.com/paper/2510.14117/) (2025)
- [Attention for Robot Touch: Tactile Saliency Prediction for Robust Sim-to-Real Tactile Control](https://mlsanity.com/paper/2307.14510/) (2023)
- [Towards Transferring Tactile-based Continuous Force Control Policies from Simulation to Robot](https://mlsanity.com/paper/2311.07245/) (2023)
- [Text2Touch: Tactile In-Hand Manipulation with LLM-Designed Reward Functions](https://mlsanity.com/paper/2509.07445/) (2025)
- [roto 2.0: The Robot Tactile Olympiad](https://mlsanity.com/paper/2605.21429/) (2026)

## Abstract (arXiv)

High-resolution optical tactile sensors are increasingly used in robotic learning environments due to their ability to capture large amounts of data directly relating to agent-environment interaction. However, there is a high barrier of entry to research in this area due to the high cost of tactile robot platforms, specialised simulation software, and sim-to-real methods that lack generality across different sensors. In this letter we extend the Tactile Gym simulator to include three new optical tactile sensors (TacTip, DIGIT and DigiTac) of the two most popular types, Gelsight-style (image-shading based) and TacTip-style (marker based). We demonstrate that a single sim-to-real approach can be used with these three different sensors to achieve strong real-world performance despite the significant differences between real tactile images. Additionally, we lower the barrier of entry to the proposed tasks by adapting them to an inexpensive 4-DoF robot arm, further enabling the dissemination of this benchmark. We validate the extended environment on three physically-interactive tasks requiring a sense of touch: object pushing, edge following and surface following. The results of our experimental validation highlight some differences between these sensors, which may help future researchers select and customize the physical characteristics of tactile sensors for different manipulations scenarios.