Tacchi 2.0: A Low Computational Cost and Comprehensive Dynamic Contact Simulator for Vision-based Tactile Sensors

With the development of robotics technology, some tactile sensors, such as vision-based sensors, have been applied to contact-rich robotics tasks. However, the durability of vision-based tactile sensors significantly increases the cost of tactile information acquisition. Utilizing simulation to generate tactile data has emerged as a reliable approach to address this issue. While data-driven methods for tactile data generation lack robustness, finite element methods (FEM) based approaches require significant computational costs. To address these issues, we integrated a pinhole camera model into the low computational cost vision-based tactile simulator Tacchi that used the Material Point Method (MPM) as the simulated method, completing the simulation of marker motion images. We upgraded Tacchi and introduced Tacchi 2.0. This simulator can simulate tactile images, marked motion images, and joint images under different motion states like pressing, slipping, and rotating. Experimental results demonstrate the reliability of our method and its robustness across various vision-based tactile sensors.

Soft Contact Simulation and Manipulation Learning of Deformable Objects with Vision-based Tactile Sensor

Deformable object manipulation is a classical and challenging research area in robotics. Compared with rigid object manipulation, this problem is more complex due to the deformation properties including elastic, plastic, and elastoplastic deformation. In this paper, we describe a new deformable object manipulation method including soft contact simulation, manipulation learning, and sim-to-real transfer. We propose a novel approach utilizing Vision-Based Tactile Sensors (VBTSs) as the end-effector in simulation to produce observations like relative position, squeezed area, and object contour, which are transferable to real robots. For a more realistic contact simulation, a new simulation environment including elastic, plastic, and elastoplastic deformations is created. We utilize RL strategies to train agents in the simulation, and expert demonstrations are applied for challenging tasks. Finally, we build a real experimental platform to complete the sim-to-real transfer and achieve a 90% success rate on difficult tasks such as cylinder and sphere. To test the robustness of our method, we use plasticine of different hardness and sizes to repeat the tasks including cylinder and sphere. The experimental results show superior performances of deformable object manipulation with the proposed method.

Simulation of Optical Tactile Sensors Supporting Slip and Rotation using Path Tracing and IMPM

Optical tactile sensors are extensively utilized in intelligent robot manipulation due to their ability to acquire high-resolution tactile information at a lower cost. However, achieving adequate reality and versatility in simulating optical tactile sensors is challenging. In this paper, we propose a simulation method and validate its effectiveness through experiments. We utilize path tracing for image rendering, achieving higher similarity to real data than the baseline method in simulating pressing scenarios. Additionally, we apply the improved Material Point Method(IMPM) algorithm to simulate the relative rest between the object and the elastomer surface when the object is in motion, enabling more accurate simulation of complex manipulations such as slip and rotation.

Accelerating Ensemble Error Bar Prediction with Single Models Fits

Ensemble models can be used to estimate prediction uncertainties in machine learning models. However, an ensemble of N models is approximately N times more computationally demanding compared to a single model when it is used for inference. In this work, we explore fitting a single model to predicted ensemble error bar data, which allows us to estimate uncertainties without the need for a full ensemble. Our approach is based on three models: Model A for predictive accuracy, Model $A_{E}$ for traditional ensemble-based error bar prediction, and Model B, fit to data from Model $A_{E}$, to be used for predicting the values of $A_{E}$ but with only one model evaluation. Model B leverages synthetic data augmentation to estimate error bars efficiently. This approach offers a highly flexible method of uncertainty quantification that can approximate that of ensemble methods but only requires a single extra model evaluation over Model A during inference. We assess this approach on a set of problems in materials science.

Tacchi: A Pluggable and Low Computational Cost Elastomer Deformation Simulator for Optical Tactile Sensors

Simulation is widely applied in robotics research to save time and resources. There have been several works to simulate optical tactile sensors that leverage either a smoothing method or Finite Element Method (FEM). However, elastomer deformation physics is not considered in the former method, whereas the latter requires a massive amount of computational resources like a computer cluster. In this work, we propose a pluggable and low computational cost simulator using the Taichi programming language for simulating optical tactile sensors, named as Tacchi . It reconstructs elastomer deformation using particles, which allows deformed elastomer surfaces to be rendered into tactile images and reveals contact information without suffering from high computational costs. Tacchi facilitates creating realistic tactile images in simulation, e.g., ones that capture wear-and-tear defects on object surfaces. In addition, the proposed Tacchi can be integrated with robotics simulators for a robot system simulation. Experiment results showed that Tacchi can produce images with better similarity to real images and achieved higher Sim2Real accuracy compared to the existing methods. Moreover, it can be connected with MuJoCo and Gazebo with only the requirement of 1G memory space in GPU compared to a computer cluster applied for FEM. With Tacchi, physical robot simulation with optical tactile sensors becomes possible. All the materials in this paper are available at https://github.com/zixichen007115/Tacchi .