Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulation
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918139615248384 |
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| author | Li, Yuyang Du, Wenxin Yu, Chang Li, Puhao Zhao, Zihang Liu, Tengyu Jiang, Chenfanfu Zhu, Yixin Huang, Siyuan |
| author_facet | Li, Yuyang Du, Wenxin Yu, Chang Li, Puhao Zhao, Zihang Liu, Tengyu Jiang, Chenfanfu Zhu, Yixin Huang, Siyuan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_12908 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulation Li, Yuyang Du, Wenxin Yu, Chang Li, Puhao Zhao, Zihang Liu, Tengyu Jiang, Chenfanfu Zhu, Yixin Huang, Siyuan Robotics Computer Vision and Pattern Recognition 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. |
| title | Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulation |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.12908 |