This work presents a fast anytime algorithm for computing globally optimal independent contact regions (ICRs). ICRs are regions such that one contact within each region enables a valid grasp. Locations of ICRs can provide guidance for grasp and manipulation planning, learning, and policy transfer. However, ICRs for modern applications have been little explored, in part due to the expense of computing them, as they have a search space exponential in the number of contacts. We present a divide and conquer algorithm based on incremental n-dimensional Delaunay triangulation that produces results with bounded suboptimality in times sufficient for real-time planning. This paper presents the base algorithm for grasps where contacts lie within a plane. Our experiments show substantial benefits over competing grasp quality metrics and speedups of 100X and more for competing approaches to computing ICRs. We explore robustness of a policy guided by ICRs and outline a path to general 3D implementation. Code will be released on publication to facilitate further development and applications.
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 …
Australian Centre for Robotic Vision（澳大利亚机器人视觉中心）；Queensland University of Technology（昆士兰科技大学）；LYRO Robotics（LYRO机器人公司）
We present the Evolved Grasping Analysis Dataset (EGAD), comprising over 2000 generated objects aimed at training and evaluating robotic visual grasp detection algorithms. The objects in EGAD are geometrically diverse, filling a space ranging from simple to complex shapes and from easy to difficult to grasp, compared to other datasets for robotic grasping, which may be limited in size or contain only a small number of object classes. Additionally, we specify a set of 49 diverse 3D-printable evaluation objects to encourage reproducible testing of robotic grasping systems across a range of compl…
GAMMA: Graspability-Aware Mobile MAnipulation Policy Learning based on Online Grasping Pose Fusion
CFCS, School of Computer Science, Peking University（北京大学计算机学院CFCS）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Galbot Tongji University（同济大学Galbot）
Institute for Robotics and Intelligent Machines（机器人与智能机器研究所）；Georgia Institute of Technology（佐治亚理工学院）
In this paper we propose an approach for efficient grasp selection for manipulation tasks of unknown objects. Even for simple tasks such as pick-and-place, a unique solution is rare to occur. Rather, multiple candidate grasps must be considered and (potentially) tested till a successful, kinematically feasible path is found. To make this process efficient, the grasps should be ordered such that those more likely to succeed are tested first. We propose to use grasp manipulability as a metric to prioritize grasps. We present results of simulation experiments which demonstrate the usefulness of o…
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…
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…
Developing Modular Grasping and Manipulation Pipeline Infrastructure to Streamline Performance Benchmarking
New England Robotics Validation and Experimentation (NERVE) Center, University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；Rutgers University（罗格斯大学）；Worcester Polytechnic University（伍斯特理工大学）；Yale University（耶鲁大学）；University of South Florida（南佛罗里达大学）
University of South Florida（南佛罗里达大学）；National Institute of Standards and Technology (NIST)（美国国家标准与技术研究院）；German Aerospace Center (DLR)（德国航空航天中心）；Worcester Polytechnic Institute (WPI)（伍斯特理工学院）
This paper discusses recent research progress in robotic grasping and manipulation in the light of the latest Robotic Grasping and Manipulation Competitions (RGMCs). We first provide an overview of past benchmarks and competitions related to the robotics manipulation field. Then, we discuss the methodology behind designing the manipulation tasks in RGMCs. We provide a detailed analysis of key challenges for each task and identify the most difficult aspects based on the competing teams' performance in recent years. We believe that such an analysis is insightful to determine the future research …
Massachusetts Institute of Technology（麻省理工学院）；University of Washington（华盛顿大学）；NVIDIA（英伟达）；University of Texas at Dallas（德克萨斯大学达拉斯分校）
6D grasping in cluttered scenes is a longstanding problem in robotic manipulation. Open-loop manipulation pipelines may fail due to inaccurate state estimation, while most end-to-end grasping methods have not yet scaled to complex scenes with obstacles. In this work, we propose a new method for end-to-end learning of 6D grasping in cluttered scenes. Our hierarchical framework learns collision-free target-driven grasping based on partial point cloud observations. We learn an embedding space to encode expert grasping plans during training and a variational autoencoder to sample diverse grasping …
This paper reports the activities and outcomes in the Workshop on Grasping and Manipulation Datasets that was organized under the International Conference on Robotics and Automation (ICRA) 2016. The half day workshop was packed with nine invited talks, 12 interactive presentations, and one panel discussion with ten panelists. This paper summarizes all the talks and presentations and recaps what has been discussed in the panels session. This summary servers as a review of recent developments in data collection in grasping and manipulation. Many of the presentations describe ongoing efforts or e…
University of Surrey（萨里大学）；University of Birmingham（伯明翰大学）
Inertial parameters characterise an object's motion under applied forces, and can provide strong priors for planning and control of robotic actions to manipulate the object. However, these parameters are not available a-priori in situations where a robot encounters new objects. In this paper, we describe and categorise the ways that a robot can identify an object's inertial parameters. We also discuss grasping and manipulation methods in which knowledge of inertial parameters is exploited in various ways. We begin with a discussion of literature which investigates how humans estimate the inert…
Hong Kong University of Science and Technology（香港科技大学）
Adaptation to various scene configurations and object properties, stability and dexterity in robotic grasping manipulation is far from explored. This work presents an origami-based shape morphing fingertip design to actively tackle the grasping stability and dexterity problems. The proposed fingertip utilizes origami as its skeleton providing degrees of freedom at desired positions and motor-driven four-bar-linkages as its transmission components to achieve a compact size of the fingertip. 3 morphing types that are commonly observed and essential in robotic grasping are studied and validated w…
Alibaba AI Labs（阿里巴巴人工智能实验室）；UC San Diego（加州大学圣迭戈分校）；University of Edinburgh（爱丁堡大学）；German Aerospace Center（德国航空航天中心）；University of South Florida（南佛罗里达大学）；Shanghai Jiaotong University（上海交通大学）；Simon Fraser University（西蒙弗雷泽大学）
In this paper, we propose a cloud-based benchmark for robotic grasping and manipulation, called the OCRTOC benchmark. The benchmark focuses on the object rearrangement problem, specifically table organization tasks. We provide a set of identical real robot setups and facilitate remote experiments of standardized table organization scenarios in varying difficulties. In this workflow, users upload their solutions to our remote server and their code is executed on the real robot setups and scored automatically. After each execution, the OCRTOC team resets the experimental setup manually. We also …
Hunan University（湖南大学）；Bielefeld University（比勒费尔德大学）；University of the West of England（西英格兰大学）
In this work, we focus on improving the robot's dexterous capability by exploiting visual sensing and adaptive force control. TeachNet, a vision-based teleoperation learning framework, is exploited to map human hand postures to a multi-fingered robot hand. We augment TeachNet, which is originally based on an imprecise kinematic mapping and position-only servoing, with a biomimetic learning-based compliance control algorithm for dexterous manipulation tasks. This compliance controller takes the mapped robotic joint angles from TeachNet as the desired goal, computes the desired joint torques. It…
Introducing Environmental Constraints to Grasping Strategies for Paper-Like Flexible Materials Using a Soft Gripper
College of Mechanical and Electrical Engineering, Nanjing University（南京大学机械与电子工程学院）；Jiangsu Key Laboratory of Bionic Materials and Equipment, Nanjing University of Aeronautics and Astronautics（江苏生物材料与设备重点实验室，南京航空航天大学）
LangGrasp: Leveraging Fine-Tuned LLMs for Language Interactive Robot Grasping with Ambiguous Instructions
School of Computer Science and Technology（计算机科学与技术学院）；Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System（湖北省智能信息处理与实时工业系统重点实验室）；Institute of Robotics and Intelligent Systems（机器人与智能系统研究院）；Wuhan University of Science and Technology（武汉理工大学）
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…
Colorado School of Mines（科罗拉多矿业学院）；Duke University（杜克大学）
In teleoperation, research has mainly focused on target approaching, where we deal with the more challenging object manipulation task by advancing the shared control technique. Appropriately manipulating an object is challenging due to the fine motion constraint requirements for a specific manipulation task. Although these motion constraints are critical for task success, they often are subtle when observing ambiguous human motion. The disembodiment problem and physical discrepancy between the human and robot hands bring additional uncertainty, further exaggerating the complications of the obj…
PartDexTOG: Generating Dexterous Task-Oriented Grasping via Language-driven Part Analysis
College of Computer Science and Technology, National University of Defense Technology（计算机科学与技术学院，国防科技大学）；College of Intelligence Science and Technology, National University of Defense Technology（智能科学与技术学院，国防科技大学）
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 object…
Khalifa University of Science and Technology（哈利法科技大学）；Kingston University（金斯顿大学）；Purdue University（普渡大学）
Slip detection is essential for robots to make robust grasping and fine manipulation. In this paper, a novel dynamic vision-based finger system for slip detection and suppression is proposed. We also present a baseline and feature based approach to detect object slips under illumination and vibration uncertainty. A threshold method is devised to autonomously sample noise in real-time to improve slip detection. Moreover, a fuzzy based suppression strategy using incipient slip feedback is proposed for regulating the grip force. A comprehensive experimental study of our proposed approaches under …
University of Warsaw（华沙大学）；IDEAS NCBR（IDEAS国家数字与区块链研究中心）；Warsaw University of Technology（华沙理工大学）；Nomagic（Nomagic公司）
Gathering real-world data from the robot quickly becomes a bottleneck when constructing a robot learning system for grasping. In this work, we design a semi-supervised grasping system that, on top of a small sample of robot experience, takes advantage of images of products to be picked, which are collected without any interactions with the robot. We validate our findings both in the simulation and in the real world. In the regime of a small number of robot training samples, taking advantage of the unlabeled data allows us to achieve performance at the level of 10-fold bigger dataset size used …
This paper introduces the i-HY Hand, an underactuated hand driven by 5 actuators that is capable of performing a wide range of grasping and in-hand manipulation tasks. This hand was designed to address the need for a durable, inexpensive, moderately dexterous hand suitable for use on mobile robots. The primary focus of this paper will be on the novel minimalistic design of i-HY, which was developed by choosing a set of target tasks around which the design of the hand was optimized. Particular emphasis is placed on the development of underactuated fingers that are capable of both firm power gra…
Massachusetts Institute of Technology (MIT)（麻省理工学院）
Modern robotic manipulation systems fall short of human manipulation skills partly because they rely on closing feedback loops exclusively around vision data, which reduces system bandwidth and speed. By developing autonomous grasping reflexes that rely on high-bandwidth force, contact, and proximity data, the overall system speed and robustness can be increased while reducing reliance on vision data. We are developing a new system built around a low-inertia, high-speed arm with nimble fingers that combines a high-level trajectory planner operating at less than 1 Hz with low-level autonomous r…
Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences（中国科学院机器人与智能制造创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Heriot-Watt University（赫瑞-瓦特大学）
Agile control of mobile manipulator is challenging because of the high complexity coupled by the robotic system and the unstructured working environment. Tracking and grasping a dynamic object with a random trajectory is even harder. In this paper, a multi-task reinforcement learning-based mobile manipulation control framework is proposed to achieve general dynamic object tracking and grasping. Several basic types of dynamic trajectories are chosen as the task training set. To improve the policy generalization in practice, random noise and dynamics randomization are introduced during the train…
Whole-Body Proprioceptive Morphing: A Modular Soft Gripper for Robust Cross-Scale Grasping
Mechanical Engineering Department at the University of Michigan-Ann Arbor（密歇根大学安娜堡分校机械工程系）；Robotics Department at the University of Michigan-Ann Arbor（密歇根大学安娜堡分校机器人学系）
``A simple handshake would give them away''. This is how Anthony Hopkins' fictional character, Dr Robert Ford, summarises a particular flaw of the 2016 science-fiction \emph{Westworld}'s hosts. In the storyline, Westworld is a futuristic theme park and the hosts are autonomous robots engineered to be indistinguishable from the human guests, except for their hands that have not been perfected yet. In another classic science-fiction saga, scientists unlock the secrets of full synthetic intelligence, Skynet, by reverse engineering a futuristic hand. In both storylines, reality inspires fiction on…
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）
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…
Robotic grasping detection is one of the most important fields in robotics, in which great progress has been made recent years with the help of convolutional neural network (CNN). However, including multiple objects in one scene can invalidate the existing CNN-based grasping detection algorithms, because manipulation relationships among objects are not considered, which are required to guide the robot to grasp things in the right order. This paper presents a new CNN architecture called Visual Manipulation Relationship Network (VMRN) to help robot detect targets and predict the manipulation rel…
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）
Istituto Italiano di Tecnologia（意大利技术研究院）；University of Naples Federico II（那不勒斯费德里科二世大学）
Manipulation in contrast to grasping is a trajectorial task that needs to use dexterous hands. Improving the dexterity of robot hands, increases the controller complexity and thus requires to use the concept of postural synergies. Inspired from postural synergies, this research proposes a new framework called kernelized synergies that focuses on the re-usability of the same subspace for precision grasping and dexterous manipulation. In this work, the computed subspace of postural synergies; parameterized by probabilistic movement primitives, is treated with kernel to preserve its grasping and …
While grasps must satisfy the grasping stability criteria, good grasps depend on the specific manipulation scenario: the object, its properties and functionalities, as well as the task and grasp constraints. In this paper, we consider such information for robot grasping by leveraging manifolds and symbolic object parts. Specifically, we introduce a new probabilistic logic module to first semantically reason about pre-grasp configurations with respect to the intended tasks. Further, a mapping is learned from part-related visual features to good grasping points. The probabilistic logic module ma…
VILAS: A VLA-Integrated Low-cost Architecture with Soft Grasping for Robotic Manipulation
Drexel University（德雷塞尔大学）；Virginia Seafood Agricultural Research and Extension Center（弗吉尼亚海鲜农业研究与推广中心）；Amazon Store Foundation AI (SFAI)（亚马逊商店基金会人工智能（SFAI））
