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

• URL: http://arxiv.org/abs/2504.12908v1
• Date: Thu, 17 Apr 2025 12:57:11 GMT
• Title: Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulation
• Authors: Yuyang Li, Wenxin Du, Chang Yu, Puhao Li, Zihang Zhao, Tengyu Liu, Chenfanfu Jiang, Yixin Zhu, Siyuan Huang,
• Abstract summary: 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.<n>Taccel provides precise physics simulation and realistic tactile signals while supporting flexible robot-sensor configurations through user-friendly APIs.<n>These capabilities position Taccel as a powerful tool for scaling up tactile robotics research and development.
• Score: 50.34179054785646
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tactile sensing is crucial for achieving human-level robotic capabilities in manipulation tasks. VBTSs have emerged as a promising solution, offering high spatial resolution and cost-effectiveness by sensing contact through camera-captured deformation patterns of elastic gel pads. However, these sensors' complex physical characteristics and visual signal processing requirements present unique challenges for robotic applications. The lack of efficient and accurate simulation tools for VBTS has significantly limited the scale and scope of tactile robotics research. Here 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. By enabling large-scale simulation and experimentation with tactile sensing, Taccel accelerates the development of more capable robotic systems, potentially transforming how robots interact with and understand their physical environment.

Related papers

• Unreal Robotics Lab: A High-Fidelity Robotics Simulator with Advanced Physics and Rendering [4.760567755149477]
  This paper presents a novel simulation framework that integrates the Unreal Engine's advanced rendering capabilities with MuJoCo's high-precision physics simulation. Our approach enables realistic robotic perception while maintaining accurate physical interactions. We benchmark visual navigation and SLAM methods within our framework, demonstrating its utility for testing real-world robustness in controlled yet diverse scenarios.
  arXiv  Detail & Related papers  (2025-04-19T01:54:45Z)
• Tacchi 2.0: A Low Computational Cost and Comprehensive Dynamic Contact Simulator for Vision-based Tactile Sensors [24.17644617805162]
  The durability of vision-based tactile sensors significantly increases the cost of tactile information acquisition.<n>We introduce a low computational cost vision-based tactile simulator Tacchi.<n>Tacchi 2.0 can simulate tactile images, marked motion images, and joint images under different motion states like pressing, slipping, and rotating.
  arXiv  Detail & Related papers  (2025-03-12T06:34:12Z)
• RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation [77.41969287400977]
  This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation. We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language. We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
  arXiv  Detail & Related papers  (2024-02-22T15:12:00Z)
• Robot Learning with Sensorimotor Pre-training [98.7755895548928]
  We present a self-supervised sensorimotor pre-training approach for robotics. Our model, called RPT, is a Transformer that operates on sequences of sensorimotor tokens. We find that sensorimotor pre-training consistently outperforms training from scratch, has favorable scaling properties, and enables transfer across different tasks, environments, and robots.
  arXiv  Detail & Related papers  (2023-06-16T17:58:10Z)
• Elastic Tactile Simulation Towards Tactile-Visual Perception [58.44106915440858]
  We propose Elastic Interaction of Particles (EIP) for tactile simulation. EIP models the tactile sensor as a group of coordinated particles, and the elastic property is applied to regulate the deformation of particles during contact. We further propose a tactile-visual perception network that enables information fusion between tactile data and visual images.
  arXiv  Detail & Related papers  (2021-08-11T03:49:59Z)
• A toolbox for neuromorphic sensing in robotics [4.157415305926584]
  We introduce a ROS (Robot Operating System) toolbox to encode and decode input signals coming from any type of sensor available on a robot. This initiative is meant to stimulate and facilitate robotic integration of neuromorphic AI.
  arXiv  Detail & Related papers  (2021-03-03T23:22:05Z)
• TACTO: A Fast, Flexible and Open-source Simulator for High-Resolution Vision-based Tactile Sensors [8.497185333795477]
  TACTO is a fast, flexible and open-source simulator for vision-based tactile sensors. It can render realistic high-resolution touch readings at hundreds of frames per second. We demonstrate TACTO on a perceptual task, by learning to predict grasp stability using touch from 1 million grasps.
  arXiv  Detail & Related papers  (2020-12-15T17:54:07Z)
• Elastic Interaction of Particles for Robotic Tactile Simulation [43.933808122317274]
  We propose Elastic Interaction of Particles (EIP), a novel framework for tactile emulation. EIP models the tactile sensor as a group of coordinated particles, and the elastic theory is applied to regulate the deformation of particles during the contact process. Experiments to verify the effectiveness of our method have been carried out on two applications.
  arXiv  Detail & Related papers  (2020-11-23T16:37:00Z)
• Point Cloud Based Reinforcement Learning for Sim-to-Real and Partial Observability in Visual Navigation [62.22058066456076]
  Reinforcement Learning (RL) represents powerful tools to solve complex robotic tasks. RL does not work directly in the real-world, which is known as the sim-to-real transfer problem. We propose a method that learns on an observation space constructed by point clouds and environment randomization.
  arXiv  Detail & Related papers  (2020-07-27T17:46:59Z)
• SAPIEN: A SimulAted Part-based Interactive ENvironment [77.4739790629284]
  SAPIEN is a realistic and physics-rich simulated environment that hosts a large-scale set for articulated objects. We evaluate state-of-the-art vision algorithms for part detection and motion attribute recognition as well as demonstrate robotic interaction tasks.
  arXiv  Detail & Related papers  (2020-03-19T00:11:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.