Simulation is widely used in robotics for system verification and large-scale data collection. However, simulating sensors, including tactile sensors, has been a long-standing challenge. In this paper, we propose Taxim, a realistic and high-speed simulation model for a vision-based tactile sensor, GelSight. A GelSight sensor uses a piece of soft elastomer as the medium of contact and embeds optical structures to capture the deformation of the elastomer, which infers the geometry and forces applied at the contact surface. We propose an example-based method for simulating GelSight: we simulate the optical response to the deformation with a polynomial look-up table. This table maps the deformed geometries to pixel intensity sampled by the embedded camera. In order to simulate the surface markers' motion that is caused by the surface stretch of the elastomer, we apply the linear elastic deformation theory and the superposition principle. The simulation model is calibrated with less than 100 data points from a real sensor. The example-based approach enables the model to easily migrate to other GelSight sensors or its variations. To the best of our knowledge, our simulation framework is the first to incorporate marker motion field simulation that derives from elastomer deformation together with the optical simulation, creating a comprehensive and computationally efficient tactile simulation framework. Experiments reveal that our optical simulation has the lowest pixel-wise intensity errors compared to prior work and can run online with CPU computing. Our code and supplementary materials are open-sourced at https://github.com/CMURoboTouch/Taxim.
This work details the design of a novel two finger robot gripper with multiple Gelsight based optical-tactile sensors covering the inner surface of the hand. The multiple Gelsight sensors can gather the surface topology of the object from multiple views simultaneously as well as can track the shear and tensile stress. In addition, other sensing modalities enable the hand to gather the thermal, acoustic and vibration information from the object being grasped. The force controlled gripper is fully actuated so that it can be used for various grasp configurations and can also be used for in-hand m…
To adapt to constantly changing environments and be safe for human interaction, robots should have compliant and soft characteristics as well as the ability to sense the world around them. Even so, the incorporation of tactile sensing into a soft compliant robot, like the Fin Ray finger, is difficult due to its deformable structure. Not only does the frame need to be modified to allow room for a vision sensor, which enables intricate tactile sensing, the robot must also retain its original mechanically compliant properties. However, adding high-resolution tactile sensors to soft fingers is dif…
In this paper we present an early prototype of the Digger Finger that is designed to easily penetrate granular media and is equipped with the GelSight sensor. Identifying objects buried in granular media using tactile sensors is a challenging task. First, particle jamming in granular media prevents downward movement. Second, the granular media particles tend to get stuck between the sensing surface and the object of interest, distorting the actual shape of the object. To tackle these challenges we present a Digger Finger prototype. It is capable of fluidizing granular media during penetration …
The synthesis of tactile sensing with compliance is essential to many fields, from agricultural usages like fruit picking, to sustainability practices such as sorting recycling, to the creation of safe home-care robots for the elderly to age with dignity. From tactile sensing, we can discern material properties, recognize textures, and determine softness, while with compliance, we are able to securely and safely interact with the objects and the environment around us. These two abilities can culminate into a useful soft robotic gripper, such as the original GelSight Fin Ray, which is able to g…
University of Texas at Austin（德克萨斯大学奥斯汀分校）；MD Anderson Cancer Center（MD安德森癌症中心）
In this paper, to address the sensitivity and durability trade-off of Vision-based Tactile Sensor (VTSs), we introduce a hyper-sensitive and high-fidelity VTS called HySenSe. We demonstrate that by solely changing one step during the fabrication of the gel layer of the GelSight sensor (as the most well-known VTS), we can substantially improve its sensitivity and durability. Our experimental results clearly demonstrate the outperformance of the HySenSe compared with a similar GelSight sensor in detecting textural details of various objects under identical experimental conditions and low interac…
Learning Force Distribution Estimation for the GelSight Mini Optical Tactile Sensor Based on Finite Element Analysis
Department of Computational Engineering, Technical University of Darmstadt（计算工程系，德累斯顿技术大学）；Department of Computer Science, Technical University of Darmstadt（计算机科学系，德累斯顿技术大学）；German Research Center for AI (DFKI)（德国人工智能研究中心）；Centre for Cognitive Science, Technical University of Darmstadt（认知科学中心，德累斯顿技术大学）；Hessian Center for Artificial Intelligence (Hessian.AI), Darmstadt（黑森人工智能中心（Hessian.AI），德累斯顿）
Rotational displacement about the grasping point is a common grasp failure when an object is grasped at a location away from its center of gravity. Tactile sensors with soft surfaces, such as GelSight sensors, can detect the rotation patterns on the contacting surfaces when the object rotates. In this work, we propose a model-based algorithm that detects those rotational patterns and measures rotational displacement using the GelSight sensor. We also integrate the rotation detection feedback into a closed-loop regrasping framework, which detects the rotational failure of grasp in an early stag…
Vision-based tactile sensors have the potential to provide important contact geometry to localize the objective with visual occlusion. However, it is challenging to measure high-resolution 3D contact geometry for a compact robot finger, to simultaneously meet optical and mechanical constraints. In this work, we present the GelSight Wedge sensor, which is optimized to have a compact shape for robot fingers, while achieving high-resolution 3D reconstruction. We evaluate the 3D reconstruction under different lighting configurations, and extend the method from 3 lights to 1 or 2 lights. We demonst…
Most current works in Sim2Real learning for robotic manipulation tasks leverage camera vision that may be significantly occluded by robot hands during the manipulation. Tactile sensing offers complementary information to vision and can compensate for the information loss caused by the occlusion. However, the use of tactile sensing is restricted in the Sim2Real research due to no simulated tactile sensors being available. To mitigate the gap, we introduce a novel approach for simulating a GelSight tactile sensor in the commonly used Gazebo simulator. Similar to the real GelSight sensor, the sim…
University of Bristol（布里斯托大学）；Bristol Robotics Laboratory（布里斯托机器人实验室）
Deep learning combined with high-resolution tactile sensing could lead to highly capable dexterous robots. However, progress is slow because of the specialist equipment and expertise. The DIGIT tactile sensor offers low-cost entry to high-resolution touch using GelSight-type sensors. Here we customize the DIGIT to have a 3D-printed sensing surface based on the TacTip family of soft biomimetic optical tactile sensors. The DIGIT-TacTip (DigiTac) enables direct comparison between these distinct tactile sensor types. For this comparison, we introduce a tactile robot system comprising a desktop arm…
Tactile sensing is inherently contact based. To use tactile data, robots need to make contact with the surface of an object. This is inefficient in applications where an agent needs to make a decision between multiple alternatives that depend the physical properties of the contact location. We propose a method to get tactile data in a non-invasive manner. The proposed method estimates the output of a tactile sensor from the depth data of the surface of the object based on past experiences. An experience dataset is built by allowing the robot to interact with various objects, collecting tactile…
Tactile sensing has seen a rapid adoption with the advent of vision-based tactile sensors. Vision-based tactile sensors provide high resolution, compact and inexpensive data to perform precise in-hand manipulation and human-robot interaction. However, the simulation of tactile sensors is still a challenge. In this paper, we built the first fully general optical tactile simulation system for a GelSight sensor using physics-based rendering techniques. We propose physically accurate light models and show in-depth analysis of individual components of our simulation pipeline. Our system outperforms…
Robot simulation has been an essential tool for data-driven manipulation tasks. However, most existing simulation frameworks lack either efficient and accurate models of physical interactions with tactile sensors or realistic tactile simulation. This makes the sim-to-real transfer for tactile-based manipulation tasks still challenging. In this work, we integrate simulation of robot dynamics and vision-based tactile sensors by modeling the physics of contact. This contact model uses simulated contact forces at the robot's end-effector to inform the generation of realistic tactile outputs. To el…
Humans perceive the world by interacting with objects, which often happens in a dynamic way. For example, a human would shake a bottle to guess its content. However, it remains a challenge for robots to understand many dynamic signals during contact well. This paper investigates dynamic tactile sensing by tackling the task of estimating liquid properties. We propose a new way of thinking about dynamic tactile sensing: by building a light-weighted data-driven model based on the simplified physical principle. The liquid in a bottle will oscillate after a perturbation. We propose a simple physics…
University of Liverpool（利物浦大学）；King’s College London（伦敦国王学院）；Monash University（莫纳什大学）
Picking up transparent objects is still a challenging task for robots. The visual properties of transparent objects such as reflection and refraction make the current grasping methods that rely on camera sensing fail to detect and localise them. However, humans can handle the transparent object well by first observing its coarse profile and then poking an area of interest to get a fine profile for grasping. Inspired by this, we propose a novel framework of vision-guided tactile poking for transparent objects grasping. In the proposed framework, a segmentation network is first used to predict t…
MagicSkin: Balancing Marker and Markerless Modes in Vision-Based Tactile Sensors with a Translucent Skin
Robot Perception Lab, Centre for Robotics Research, Department of Engineering, King’s College London（机器人感知实验室，机器人研究研究中心，工程系，伦敦国王学院）；Imperial College London（帝国理工学院伦敦校区）
University of Liverpool（利物浦大学）；Northwest Institute of Mechanical and Electrical Engineering（西北机电工程研究所）
Tactile sensors can provide detailed contact in-formation that can facilitate robots to perform dexterous, in-hand manipulation tasks. One of the primitive but important tasks is surface following that is a key feature for robots while exploring unknown environments or workspace of inaccurate modeling. In this paper, we propose a novel end-to-end learning strategy, by directly mapping the raw tactile data acquired from a GelSight tactile sensor to the motion of the robot end-effector.Experiments on a KUKA youBot platform equipped with theGelSight sensor show that 80% of the actions generated b…
Advances in Compliance Detection: Novel Models Using Vision-Based Tactile Sensors
L3S Research Center , Leibniz Universität Hannover（L3S研究所以及汉诺威莱布尼茨大学）；Department of Biomedical Engineering and the School of Brain Sciences, Ben-Gurion University of the Negev（生物医学工程系和脑科学学院，本·古里安大学）
Massachusetts Institute of Technology（麻省理工学院）；Tsinghua University（清华大学）
Soft robots offer significant advantages in adaptability, safety, and dexterity compared to conventional rigid-body robots. However, it is challenging to equip soft robots with accurate proprioception and tactile sensing due to their high flexibility and elasticity. In this work, we describe the development of a vision-based proprioceptive and tactile sensor for soft robots called GelFlex, which is inspired by previous GelSight sensing techniques. More specifically, we develop a novel exoskeleton-covered soft finger with embedded cameras and deep learning methods that enable high-resolution pr…
An industrial connector insertion task requires submillimeter positioning and grasp pose compensation for a plug. Thus, highly accurate estimation of the relative pose between a plug and socket is fundamental for achieving the task. World models are promising technologies for visuomotor control because they obtain appropriate state representation to jointly optimize feature extraction and latent dynamics model. Recent studies show that the NewtonianVAE, a type of the world model, acquires latent space equivalent to mapping from images to physical coordinates. Proportional control can be achiev…
High resolution tactile sensors are often bulky and have shape profiles that make them awkward for use in manipulation. This becomes important when using such sensors as fingertips for dexterous multi-fingered hands, where boxy or planar fingertips limit the available set of smooth manipulation strategies. High resolution optical based sensors such as GelSight have until now been constrained to relatively flat geometries due to constraints on illumination geometry.Here, we show how to construct a rounded fingertip that utilizes a form of light piping for directional illumination. Our sensors c…
Massachusetts Institute of Technology（麻省理工学院）；Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
针对柔性结构与高分辨率触觉感知协同设计的难题，提出融合有限元力学仿真与PBR光学仿真的GelSight Fin Ray传感器端到端设计框架，可优化多类设计参数，缩短原型周期，验证日常物品抓取效用。
This work describes the development of a high-resolution tactile-sensing finger for robot grasping. This finger, inspired by previous GelSight sensing techniques, features an integration that is slimmer, more robust, and with more homogeneous output than previous vision-based tactile sensors. To achieve a compact integration, we redesign the optical path from illumination source to camera by combining light guides and an arrangement of mirror reflections. We parameterize the optical path with geometric design variables and describe the tradeoffs between the finger thickness, the depth of field…
Carnegie Mellon University（卡内基梅隆大学）；Brigham Young University（杨百翰大学）
Knowledge of 3-D object shape is of great importance to robot manipulation tasks, but may not be readily available in unstructured environments. While vision is often occluded during robot-object interaction, high-resolution tactile sensors can give a dense local perspective of the object. However, tactile sensors have limited sensing area and the shape representation must faithfully approximate non-contact areas. In addition, a key challenge is efficiently incorporating these dense tactile measurements into a 3-D mapping framework. In this work, we propose an incremental shape mapping method …
University of Bristol（布里斯托大学）；Bristol Robotics Laboratory（布里斯托机器人实验室）
High-resolution optical tactile sensors are increasingly used in robotic learning environments due to their ability to capture large amounts of data directly relating to agent-environment interaction. However, there is a high barrier of entry to research in this area due to the high cost of tactile robot platforms, specialised simulation software, and sim-to-real methods that lack generality across different sensors. In this letter we extend the Tactile Gym simulator to include three new optical tactile sensors (TacTip, DIGIT and DigiTac) of the two most popular types, Gelsight-style (image-sh…
Multi-Resolution Tactile Imitation Learning for Contact-Rich Robotic Manipulation
Interactive Robot Perception & Learning, TU Darmstadt（互动机器人感知与学习，图腾达姆施塔特大学）；Intelligent Autonomous Systems, TU Darmstadt（智能自主系统，图腾达姆施塔特大学）；Hessian AI（黑森人工智能）；Robotics Institute Germany（德国机器人研究所）
University of Applied Sciences Augsburg（奥格斯堡应用技术大学）；Ulster University（阿尔斯特大学）
A robust grip is key to successful manipulation and joining of work pieces involved in any industrial assembly process. Stability of a grip depends on geometric and physical properties of the object as well as the gripper itself. Current state-of-the-art algorithms can usually predict if a grip would fail. However, they are not able to predict the force at which the gripped object starts to slip, which is critical as the object might be subjected to external forces, e.g. when joining it with another object. This research project aims to develop a AI-based approach for a grip metric based on ta…
University of California, Berkeley（加利福尼亚大学伯克利分校）；Facebook AI Research（Facebook人工智能研究院）
Touch sensing is widely acknowledged to be important for dexterous robotic manipulation, but exploiting tactile sensing for continuous, non-prehensile manipulation is challenging. General purpose control techniques that are able to effectively leverage tactile sensing as well as accurate physics models of contacts and forces remain largely elusive, and it is unclear how to even specify a desired behavior in terms of tactile percepts. In this paper, we take a step towards addressing these issues by combining high-resolution tactile sensing with data-driven modeling using deep neural network dyn…
