# Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulation

arXiv: https://arxiv.org/abs/2504.12908 · HTML: https://mlsanity.com/paper/2504.12908/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 69 references that are in the map.
- First posted on arXiv 17 Apr 2025.

## Builds on (most cited first, 30 of 36)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [SAPIEN: A SimulAted Part-based Interactive ENvironment](https://mlsanity.com/paper/2003.08515/) (2020)
- [Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics](https://mlsanity.com/paper/1703.09312/) (2017)
- [ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI](https://mlsanity.com/paper/2410.00425/) (2024)
- [ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging](https://mlsanity.com/paper/1904.06830/) (2019)
- [More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch](https://mlsanity.com/paper/1805.11085/) (2018)
- [ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics](https://mlsanity.com/paper/1810.01054/) (2018)
- [Synthesizing Diverse and Physically Stable Grasps with Arbitrary Hand Structures using Differentiable Force Closure Estimator](https://mlsanity.com/paper/2104.09194/) (2021)
- [TACTO: A Fast, Flexible, and Open-source Simulator for High-Resolution Vision-based Tactile Sensors](https://mlsanity.com/paper/2012.08456/) (2020)
- [Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense](https://mlsanity.com/paper/2004.09044/) (2020)
- [GenDexGrasp: Generalizable Dexterous Grasping](https://mlsanity.com/paper/2210.00722/) (2022)
- [Cable Manipulation with a Tactile-Reactive Gripper](https://mlsanity.com/paper/1910.02860/) (2019)
- [Taxim: An Example-based Simulation Model for GelSight Tactile Sensors](https://mlsanity.com/paper/2109.04027/) (2021)
- [Improved GelSight Tactile Sensor for Measuring Geometry and Slip](https://mlsanity.com/paper/1708.00922/) (2017)
- [TacSL: A Library for Visuotactile Sensor Simulation and Learning](https://mlsanity.com/paper/2408.06506/) (2024)
- [DIFFTACTILE: A Physics-based Differentiable Tactile Simulator for Contact-rich Robotic Manipulation](https://mlsanity.com/paper/2403.08716/) (2024)
- [Tactile Dexterity: Manipulation Primitives with Tactile Feedback](https://mlsanity.com/paper/2002.03236/) (2020)
- [Grasp Multiple Objects with One Hand](https://mlsanity.com/paper/2310.15599/) (2023)
- [9DTact: A Compact Vision-Based Tactile Sensor for Accurate 3D Shape Reconstruction and Generalizable 6D Force Estimation](https://mlsanity.com/paper/2308.14277/) (2023)
- [Generation of GelSight Tactile Images for Sim2Real Learning](https://mlsanity.com/paper/2101.07169/) (2021)
- [Tac2Pose: Tactile Object Pose Estimation from the First Touch](https://mlsanity.com/paper/2204.11701/) (2022)
- [Visual-Tactile Sensing for In-Hand Object Reconstruction](https://mlsanity.com/paper/2303.14498/) (2023)
- [Tac-Man: Tactile-Informed Prior-Free Manipulation of Articulated Objects](https://mlsanity.com/paper/2403.01694/) (2024)
- [Tacchi: A Pluggable and Low Computational Cost Elastomer Deformation Simulator for Optical Tactile Sensors](https://mlsanity.com/paper/2301.08343/) (2023)
- [Dynamic Reconstruction of Hand-Object Interaction with Distributed Force-aware Contact Representation](https://mlsanity.com/paper/2411.09572/) (2024)
- [Embedding high-resolution touch across robotic hands enables adaptive human-like grasping](https://mlsanity.com/paper/2412.14482/) (2024)
- [Exploiting Kinematic Redundancy for Robotic Grasping of Multiple Objects](https://mlsanity.com/paper/2303.01868/) (2023)
- [Understanding Dynamic Tactile Sensing for Liquid Property Estimation](https://mlsanity.com/paper/2205.08771/) (2022)
- [TacIPC: Intersection- and Inversion-free FEM-based Elastomer Simulation For Optical Tactile Sensors](https://mlsanity.com/paper/2311.05843/) (2023)

## Built on by (most cited first, 8 of 8)

- [A Survey of Robotic Navigation and Manipulation with Physics Simulators in the Era of Embodied AI](https://mlsanity.com/paper/2505.01458/) (2025)
- [Simultaneous Tactile-Visual Perception for Learning Multimodal Robot Manipulation](https://mlsanity.com/paper/2512.09851/) (2025)
- [Data Pyramid for Embodied Manipulation: A Survey](https://mlsanity.com/paper/2607.24744/) (2026)
- [TacCoRL: Integrating Tactile Feedback into VLA via Simulation](https://mlsanity.com/paper/2606.11743/) (2026)
- [Blind Dexterous Grasping via Real2Sim2Real Tactile Policy Learning](https://mlsanity.com/paper/2606.11767/) (2026)
- [Tac2Real: Reliable and GPU Visuotactile Simulation for Online Reinforcement Learning and Zero-Shot Real-World Deployment](https://mlsanity.com/paper/2603.28475/) (2026)
- [ControlTac: Scaling Tactile Data with Physically Controlled Tactile Image Generation](https://mlsanity.com/paper/2505.20498/) (2025)
- [DexNinja: Learning Robust Dexterous Cutting Policy with a Real-to-Sim-to-Real Data Engine](https://mlsanity.com/paper/2610.17208/) (2026)

## Abstract (arXiv)

Tactile sensing is crucial for achieving human-level robotic capabilities in manipulation tasks. As a promising solution, Vision-Based Tactile Sensors (VBTSs) offer high spatial resolution and cost-effectiveness, but present unique challenges in robotics for their complex physical characteristics and visual signal processing requirements. The lack of efficient and accurate simulation tools for VBTSs has significantly limited the scale and scope of tactile robotics research. We present Taccel, a high-performance simulation platform that integrates IPC and ABD to model robots, tactile sensors, and objects with both accuracy and unprecedented speed, achieving an 18-fold acceleration over real-time across thousands of parallel environments. Unlike previous simulators that operate at sub-real-time speeds with limited parallelization, Taccel provides precise physics simulation and realistic tactile signals while supporting flexible robot-sensor configurations through user-friendly APIs. Through extensive validation in object recognition, robotic grasping, and articulated object manipulation, we demonstrate precise simulation and successful sim-to-real transfer. These capabilities position Taccel as a powerful tool for scaling up tactile robotics research and development, potentially transforming how robots interact with and understand their physical environment.