TACTO: A Fast, Flexible, and Open-source Simulator for High-Resolution Vision-based Tactile Sensors (2012.08456v2)

Abstract: Simulators perform an important role in prototyping, debugging, and benchmarking new advances in robotics and learning for control. Although many physics engines exist, some aspects of the real world are harder than others to simulate. One of the aspects that have so far eluded accurate simulation is touch sensing. To address this gap, we present TACTO - a fast, flexible, and open-source simulator for vision-based tactile sensors. This simulator allows to render realistic high-resolution touch readings at hundreds of frames per second, and can be easily configured to simulate different vision-based tactile sensors, including DIGIT and OmniTact. In this paper, we detail the principles that drove the implementation of TACTO and how they are reflected in its architecture. We demonstrate TACTO on a perceptual task, by learning to predict grasp stability using touch from 1 million grasps, and on a marble manipulation control task. Moreover, we provide a proof-of-concept that TACTO can be successfully used for Sim2Real applications. We believe that TACTO is a step towards the widespread adoption of touch sensing in robotic applications, and to enable machine learning practitioners interested in multi-modal learning and control. TACTO is open-source at https://github.com/facebookresearch/tacto.

• The paper introduces TACTO, an open-source simulator that renders high-resolution tactile sensor data at over 200 frames per second.
• The paper leverages OpenGL and integrates with physics engines like PyBullet to realistically simulate diverse sensor designs and detailed surface interactions.
• Experimental validation using over 1 million simulated grasps and in-hand manipulation tasks highlights TACTO’s impact on tactile robotics research.

TACTO: A Simulator for Vision-based Tactile Sensors

The paper presents TACTO, a simulator designed to address the challenge of simulating high-resolution vision-based tactile sensors, an area traditionally difficult to emulate accurately. This open-source tool is engineered to provide efficient and flexible simulation capabilities, making it invaluable for researchers exploring tactile sensing within robotics and machine learning domains.

Architectural Insights

TACTO was developed with specific design objectives: high throughput, flexibility, realism, and ease of use. By leveraging OpenGL for rendering, TACTO achieves impressive performance, rendering tactile imprints at over 200 frames per second in some scenarios. This architectural choice allows realistic simulation capabilities, such as simulating distinct tactile sensor designs, including recent sensors like OmniTact and DIGIT. The platform integrates smoothly with various physics engines, showcasing its adaptability.

Key Features and Implementation

TACTO's architecture prioritizes synchronization between physics simulators like PyBullet and back-end rendering engines like Pyrender. It supports rendering from depth models, which is crucial for applications requiring detailed surface interactions and provides a procedure for calibrating simulations with real-world sensor data. This feature ensures simulations are finely tuned to mirror real-world tactile readings, enhancing their applicability in Sim2Real contexts.

Using modular configuration, TACTO can model diverse sensor geometries and lighting conditions, making it versatile for varied experimental needs. The simulator's capacity to generate realistic tactile data facilitates significant developments in robotic touch perception and tactile control systems.

Experimental Validation

The paper validates TACTO through experiments on grasp stability learning and in-hand marble manipulation tasks. By simulating tactile data from 1 million grasps, the researchers demonstrated TACTO's capability to handle extensive datasets, producing insights that align with and extend beyond existing real-world data constraints. Additionally, a control task involving marble manipulation highlights the simulator's effectiveness in tactile-driven control applications.

Implications and Prospects

TACTO's development marks substantial progress towards simulating high-fidelity tactile interactions, a critical step for robotics and automation. The simulator opens pathways for machine learning practitioners to develop and test multisensory algorithms without immediate need for physical sensors, thereby accelerating research and development cycles. Moreover, TACTO's capabilities suggest potential in enhancing tactile sensor design by allowing hardware developers to prototype and refine their designs in a virtual environment.

For future developments, integrating more comprehensive force-deformation dynamics and exploring advanced learning techniques for touch-sensing tasks could further enhance TACTO's utility. The provision of TACTO as an open-source tool aims to stimulate advancements across robotics, tactile sensing, and machine learning communities, offering a robust platform for future research and innovation.