Grasping in the Dark: Zero-Shot Object Grasping Using Tactile Feedback
Perception and Robotics Group, University of Maryland, College Park（马里兰大学帕克分校感知与机器人小组）；University of Maryland, College Park（马里兰大学帕克分校）；University of Maryland（马里兰大学）
Grasping and manipulating a wide variety of objects is a fundamental skill that would determine the success and wide spread adaptation of robots in homes. Several end-effector designs for robust manipulation have been proposed but they mostly work when provided with prior information about the objects or equipped with external sensors for estimating object shape or size. Such approaches are limited to many-shot or unknown objects and are prone to estimation errors from external estimation systems. We propose an approach to grasp and manipulate previously unseen or zero-shot objects: the objects without any prior of their shape, size, material and weight properties, using only feedback from tactile sensors which is contrary to the state-of-the-art. Such an approach provides robust manipulation of objects either when the object model is not known or when it is estimated incorrectly from an external system. Our approach is inspired by the ideology of how animals or humans manipulate objects, i.e., by using feedback from their skin. Our grasping and manipulation revolves around the simple notion that objects slip if not grasped stably. This slippage can be detected and counteracted for a robust grasp that is agnostic to the type, shape, size, material and weight of the object. At the crux of our approach is a novel tactile feedback based controller that detects and compensates for slip during grasp. We successfully evaluate and demonstrate our proposed approach on many real world experiments using the Shadow Dexterous Hand equipped with BioTac SP tactile sensors for different object shapes, sizes, weights and materials. We obtain an overall success rate of 73.5%
The University of Melbourne（墨尔本大学）；St. Vincent’s Hospital（圣文森特医院）
Tactile-based blind grasping addresses realistic robotic grasping in which the hand only has access to proprioceptive and tactile sensors. The robotic hand has no prior knowledge of the object/grasp properties, such as object weight, inertia, and shape. There exists no manipulation controller that rigorously guarantees object manipulation in such a setting. Here, a robust control law is proposed for object manipulation in tactile-based blind grasping. The analysis ensures semi-global asymptotic and exponential stability in the presence of model uncertainties and external disturbances that are …
In the robotic crop harvesting environment, foreign objects intrusion in the gripper workspace is frequently occurring and unignorable, however, rarely addressed. This paper presents a novel intelligent robotic grasping method capable of handling obstacle interference, which is the first of its kind in the literature. The proposed method combines deep learning algorithms with low-cost tactile sensing hardware on a multi-DoF soft robotic gripper. Through experimental validations, the proposed method demonstrated promising performance in distinguishing various grasping scenarios. The 4-finger in…
This work provides an architecture that incorporates depth and tactile information to create rich and accurate 3D models useful for robotic manipulation tasks. This is accomplished through the use of a 3D convolutional neural network (CNN). Offline, the network is provided with both depth and tactile information and trained to predict the object's geometry, thus filling in regions of occlusion. At runtime, the network is provided a partial view of an object. Tactile information is acquired to augment the captured depth information. The network can then reason about the object's geometry by uti…
The sense of touch plays a key role in enabling humans to understand and interact with surrounding environments. For robots, tactile sensing is also irreplaceable. While interacting with objects, tactile sensing provides useful information for the robot to understand the object, such as distributed pressure, temperature, vibrations and texture. During robot grasping, vision is often occluded by its end-effectors, whereas tactile sensing can measure areas that are not accessible by vision. In the past decades, a number of tactile sensors have been developed for robots and used for different rob…
TouchDrive: Electronics-Free Tactile Sensing Interface for Assistive Grasping
Division of Microsystems Technology, Department of Electrical Engineering（微系统技术部门）；Cyber-Physical Systems Lab, Department of Information Technology（网络物理系统实验室）
NVIDIA（英伟达）；Georgia Institute of Technology（佐治亚理工学院）；University of Utah（犹他大学）
Using simulation to train robot manipulation policies holds the promise of an almost unlimited amount of training data, generated safely out of harm's way. One of the key challenges of using simulation, to date, has been to bridge the reality gap, so that policies trained in simulation can be deployed in the real world. We explore the reality gap in the context of learning a contextual policy for multi-fingered robotic grasping. We propose a Grasping Objects Approach for Tactile (GOAT) robotic hands, learning to overcome the reality gap problem. In our approach we use human hand motion demonst…
Tactile perception is an essential ability of intelligent robots in interaction with their surrounding environments. This perception as an intermediate level acts between sensation and action and has to be defined properly to generate suitable action in response to sensed data. In this paper, we propose a feedback approach to address robot grasping task using force-torque tactile sensing. While visual perception is an essential part for gross reaching, constant utilization of this sensing modality can negatively affect the grasping process with overwhelming computation. In such case, human bei…
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…
Learning Stable Robot Grasping with Transformer-based Tactile Control Policies
Italian Institute of Technology（意大利技术研究院）；University of Genoa（热那亚大学）；National University of Singapore（新加坡国立大学）；Institute for Infocomm Research (IR), A*STAR（新加坡科技研究局信息通信研究所）；King’s College London（伦敦国王学院）
Guangdong University of Technology（广东工业大学）；South China University of Technology（华南理工大学）
Humans can accurately determine whether the object in hand has slipped or not by visual and tactile perception. However, it is still a challenge for robots to detect in-hand object slip through visuo-tactile fusion. To address this issue, a novel visuo-tactile fusion deep neural network is proposed to detect slip, which is a time-dependent continuous action. By using the multi-scale temporal convolution network (MS-TCN) to extract the temporal features of visual and tactile data, the slip can be detected effectively. In this paper, a 7-dregree-of-freedom (7-DoF) robot manipulator equipped with…
Computer Science and Artificial Intelligence Laboratory (CSAIL), MIT（麻省理工学院计算机科学与人工智能实验室（CSAIL））
Slip detection plays a vital role in robotic manipulation and it has long been a challenging problem in the robotic community. In this paper, we propose a new method based on deep neural network (DNN) to detect slip. The training data is acquired by a GelSight tactile sensor and a camera mounted on a gripper when we use a robot arm to grasp and lift 94 daily objects with different grasping forces and grasping positions. The DNN is trained to classify whether a slip occurred or not. To evaluate the performance of the DNN, we test 10 unseen objects in 152 grasps. A detection accuracy as high as …
Robotic manipulation of cloth has applications ranging from fabrics manufacturing to handling blankets and laundry. Cloth manipulation is challenging for robots largely due to their high degrees of freedom, complex dynamics, and severe self-occlusions when in folded or crumpled configurations. Prior work on robotic manipulation of cloth relies primarily on vision sensors alone, which may pose challenges for fine-grained manipulation tasks such as grasping a desired number of cloth layers from a stack of cloth. In this paper, we propose to use tactile sensing for cloth manipulation; we attach a…
We describe the grasping and manipulation strategy that we employed at the autonomous track of the Robotic Grasping and Manipulation Competition at IROS 2016. A salient feature of our architecture is the tight coupling between visual (Asus Xtion) and tactile perception (Robotic Materials), to reduce the uncertainty in sensing and actuation. We demonstrate the importance of tactile sensing and reactive control during the final stages of grasping using a Kinova Robotic arm. The set of tools and algorithms for object grasping presented here have been integrated into the open-source Robot Operatin…
Visual-tactile manipulation to collect household waste in outdoor
Instituto Universitario de Investigación Informática, Universidad de Alicante（阿利坎特大学大学信息学研究学院）；Universidad de Alicante（阿利坎特大学）；Universidad Miguel Hernández（米格尔·埃尔南德斯大学）
Southern University of Science and Technology（南方科技大学）；University of Hong Kong（香港大学）
Over the past few decades, efforts have been made towards robust robotic grasping, and therefore dexterous manipulation. The soft gripper has shown their potential in robust grasping due to their inherent properties-low, control complexity, and high adaptability. However, the deformation of the soft gripper when interacting with objects bring inaccuracy of grasped objects, which causes instability for robust grasping and further manipulation. In this paper, we present an omni-directional adaptive soft finger that can sense deformation based on embedded optical fibers and the application of mac…
University of Utah（犹他大学）；NVIDIA Corporation（英伟达公司）
While tactile skins have been shown to be useful for detecting collisions between a robotic arm and its environment, they have not been extensively used for improving robotic grasping and in-hand manipulation. We propose a novel sensor design for use in covering existing multi-fingered robot hands. We analyze the performance of four different piezoresistive materials using both fabric and anti-static foam substrates in benchtop experiments. We find that although the piezoresistive foam was designed as packing material and not for use as a sensing substrate, it performs comparably with fabrics …
AllTact Fin Ray: A Compliant Robot Gripper with Omni-Directional Tactile Sensing
Department of Mechanical Engineering, Tsinghua University（清华大学机械工程系）；Tsinghua-Berkeley Shenzhen Institute（清华大学-伯克利深圳研究院）；Institute of Data and Information, Shenzhen International Graduate School, Tsinghua University（清华大学数据与信息研究所、深圳国际研究生院）
本文提出 AllTact Fin Ray 柔顺机器人夹爪，通过相机捕捉指体形变，并提出基于边缘特征重建全局形变与动态参考图像亮度差异计算接触几何的新方法，实现了全向触觉感知，在接触检测、力估计和精确操作等任务中验证了有效性。
University of California, Berkeley（加州大学伯克利分校）；Massachusetts Institute of Technology（麻省理工学院）
For humans, the process of grasping an object relies heavily on rich tactile feedback. Most recent robotic grasping work, however, has been based only on visual input, and thus cannot easily benefit from feedback after initiating contact. In this paper, we investigate how a robot can learn to use tactile information to iteratively and efficiently adjust its grasp. To this end, we propose an end-to-end action-conditional model that learns regrasping policies from raw visuo-tactile data. This model -- a deep, multimodal convolutional network -- predicts the outcome of a candidate grasp adjustmen…
We investigate how high-resolution tactile sensors can be utilized in combination with vision and depth sensing, to improve grasp stability prediction. Recent advances in simulating high-resolution tactile sensing, in particular the TACTO simulator, enabled us to evaluate how neural networks can be trained with a combination of sensing modalities. With the large amounts of data needed to train large neural networks, robotic simulators provide a fast way to automate the data collection process. We expand on the existing work through an ablation study and an increased set of objects taken from t…
Robotic grasp detection is a fundamental capability for intelligent manipulation in unstructured environments. Previous work mainly employed visual and tactile fusion to achieve stable grasp, while, the whole process depending heavily on regrasping, which wastes much time to regulate and evaluate. We propose a novel way to improve robotic grasping: by using learned tactile knowledge, a robot can achieve a stable grasp from an image. First, we construct a prior tactile knowledge learning framework with novel grasp quality metric which is determined by measuring its resistance to external pertur…
University of Maryland（马里兰大学）；Institute for Advanced Computer Studies（高级计算机研究所）
Grasping objects requires tight integration between visual and tactile feedback. However, there is an inherent difference in the scale at which both these input modalities operate. It is thus necessary to be able to analyze tactile feedback in isolation in order to gain information about the surface the end-effector is operating on, such that more fine-grained features may be extracted from the surroundings. For tactile perception of the robot, inspired by the concept of the tactile flow in humans, we present the computational tactile flow to improve the analysis of the tactile feedback in rob…
SegGrasp: Zero-Shot Task-Oriented Grasping via Semantic and Geometric Guided Segmentation
Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；University of Hong Kong（香港大学）；Zhejiang University（浙江大学）；MEGVII Technology（旷视科技）；Waseda University（早稻田大学）
Robotic Perception with a Large Tactile-Vision-Language Model for Physical Property Inference
College of Big Data and Internet, Shenzhen Technology University, China（大数据与互联网学院，深圳科技大学，中国）；Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, China（中德智能制造学院，深圳科技大学，中国）；Robotics Department, Institute of Smart Systems and Artificial Intelligence (ISSAI), Nazarbayev University, Kazakhstan（机器人系，智能系统与人工智能研究所（ISSAI），纳扎尔巴耶夫大学，哈萨克斯坦）；Foundation Robotics Labs, Germany（基础机器人实验室，德国）
University of Hong Kong（香港大学）；Southern University of Science and Technology（南方科技大学）；SUSTech Institute of Intelligent Manufacturing（南方科技大学智能制造研究院）
This paper presents a novel design of a soft tactile finger with omni-directional adaptation using multi-channel optical fibers for rigid-soft interactive grasping. Machine learning methods are used to train a model for real-time prediction of force, torque, and contact using the tactile data collected. We further integrated such fingers in a reconfigurable gripper design with three fingers so that the finger arrangement can be actively adjusted in real-time based on the tactile data collected during grasping, achieving the process of rigid-soft interactive grasping. Detailed sensor calibratio…
Learning Robust Grasping Strategy Through Tactile Sensing and Adaption Skill
School of Power and Mechanical Engineering, Wuhan University（武汉大学动力与机械学院）；School of Technological Sciences, Wuhan University（武汉大学技术科学学院）；School of Microelectronics, Wuhan University（武汉大学微电子学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）
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…
