Templates for pre-grasp sliding interactions

In manipulation tasks that require object acquisition, pre-grasp interaction such as sliding adjusts the object in the environment before grasping. This change in object placement can improve grasping success by making desired grasps reachable. However, the additional sliding action prior to grasping introduces more complexity to the motion planning process, since the hand pose relative to the object does not need to remain fixed during the pre-grasp interaction. Furthermore, anthropomorphic hands in humanoid robots have several degrees of freedom that could be utilized to improve the object interaction beyond a fixed grasp shape. We present a framework for synthesizing pre-grasp interactions for high-dimensional anthropomorphic manipulators. The motion planning is tractable because information from pre-grasp manipulation examples reduces the search space to promising hand poses and shapes. In particular, we show the value of organizing the example data according to object category templates. The template information focuses the search based on the object features, resulting in increased success of adapting a template pose and decreased planning time.     

Towards informative sensor-based grasp planning

This paper proposes a probabilistic framework for sensor-based grasping and describes how information about object attributes, such as position and orientation, can be updated using on-line sensor information gained during grasping. This allows learning about the target object even with a failed grasp, leading to replanning with improved performance at each successive attempt. Two grasp planning approaches utilizing the framework are proposed. Firstly, an approach maximizing the expected posterior stability of a grasp is suggested. Secondly, the approach is extended to use an entropy-based explorative procedure, which allows gathering more information when the current belief about the grasp stability does not allow robust grasping. In the framework, both object and grasp attributes as well as the stability of the grasp and on-line sensor information are represented by probabilistic models. Experiments show that the probabilistic treatment of grasping allows improving the probability of success in a series of grasping attempts. Moreover, experimental results on a real platform using the basic stability maximizing approach not only validate the proposed probabilistic framework but also show that under large initial uncertainties, explorative actions help to achieve successful grasps faster.     

Demonstration-based learning and control for automatic grasping

We present a method for automatic grasp generation based on object shape primitives in a Programming by Demonstration framework. The system first recognizes the grasp performed by a demonstrator as well as the object it is applied on and then generates a suitable grasping strategy on the robot. We start by presenting how to model and learn grasps and map them to robot hands. We continue by performing dynamic simulation of the grasp execution with a focus on grasping objects whose pose is not perfectly known.     

A 3D shape segmentation approach for robot grasping by parts

Neuro-psychological findings have shown that human perception of objects is based on part decomposition. Most objects are made of multiple parts which are likely to be the entities actually involved in grasp affordances. Therefore, automatic object recognition and robot grasping should take advantage from 3D shape segmentation. This paper presents an approach toward planning robot grasps across similar objects by part correspondence. The novelty of the method lies in the topological decomposition of objects that enables high-level semantic grasp planning.In particular, given a 3D model of an object, the representation is initially segmented by computing its Reeb graph. Then, automatic object recognition and part annotation are performed by applying a shape retrieval algorithm. After the recognition phase, queries are accepted for planning grasps on individual parts of the object. Finally, a robot grasp planner is invoked for finding stable grasps on the selected part of the object. Grasps are evaluated according to a widely used quality measure. Experiments performed in a simulated environment on a reasonably large dataset show the potential of topological segmentation to highlight candidate parts suitable for grasping.     

Efficient models for grasp planning with a multi-fingered hand

This paper presents a simple grasp planning method for a multi-fingered hand. Its purpose is to compute a context-independent and dense set or list of grasps, instead of just a small set of grasps regarded as optimal with respect to a given criterion. By context-independent, we mean that only the robot hand and the object to grasp are considered. The environment and the position of the robot base with respect to the object are considered in a further stage. Such a dense set can be computed offline and then used to let the robot quickly choose a grasp adapted to a specific situation. This can be useful for manipulation planning of pick-and-place tasks. Another application is human–robot interaction when the human and robot have to hand over objects to each other. If human and robot have to work together with a predefined set of objects, grasp lists can be employed to allow a fast interaction.The proposed method uses a dense sampling of the possible hand approaches based on a simple but efficient shape feature. As this leads to many finger inverse kinematics tests, hierarchical data structures are employed to reduce the computation times. The data structures allow a fast determination of the points where the fingers can realize a contact with the object surface. The grasps are ranked according to a grasp quality criterion so that the robot will first parse the list from best to worse quality grasps, until it finds a grasp that is valid for a particular situation.     

一类Power抓持接触力分解的一般表达式

与灵巧抓持相比,Power抓持可承受较大的外部载荷 ,能够更稳定地抓持物体．但由于抓持机构与物体间的约束较多,且接触点可出现在抓持机 构中活动度有限的构件上,因此不能采用已有的适用于灵巧抓持的接触力分解方法对其接触 力进行分解．本文在对一类Power抓持机构的结构特征进行分析的基础上,适当地建立了接 触坐标系和物体坐标系．通过对接触力空间进行分解,给出了抓持接触力分解的一般表达式 ．并根据该表达式,建立了此类Power抓持的外部载荷与关节力矩及接触力之间的显函数关 系．     

Semantic grasping: planning task-specific stable robotic grasps

We present an example-based planning framework to generate semantic grasps, stable grasps that are functionally suitable for specific object manipulation tasks. We propose to use partial object geometry, tactile contacts, and hand kinematic data as proxies to encode task-related constraints, which we call semantic constraints. We introduce a semantic affordance map, which relates local geometry to a set of predefined semantic grasps that are appropriate to different tasks. Using this map, the pose of a robot hand with respect to the object can be estimated so that the hand is adjusted to achieve the ideal approach direction required by a particular task. A grasp planner is then used to search along this approach direction and generate a set of final grasps which have appropriate stability, tactile contacts, and hand kinematics. We show experiments planning semantic grasps on everyday objects and applying these grasps with a physical robot.     

Data-driven grasp synthesis using shape matching and task-based pruning

Human grasps, especially whole-hand grasps, are difficult to animate because of the high number of degrees of freedom of the hand and the need for the hand to conform naturally to the object surface. Captured human motion data provides us with a rich source of examples of natural grasps. However, for each new object, we are faced with the problem of selecting the best grasp from the database and adapting it to that object. This paper presents a data-driven approach to grasp synthesis. We begin with a database of captured human grasps. To identify candidate grasps for a new object, we introduce a novel shape matching algorithm that matches hand shape to object shape by identifying collections of features having similar relative placements and surface normals. This step returns many grasp candidates, which are clustered and pruned by choosing the grasp best suited for the intended task. For pruning undesirable grasps, we develop an anatomically-based grasp quality measure specific to the human hand. Examples of grasp synthesis are shown for a variety of objects not present in the original database. This algorithm should be useful both as an animator tool for posing the hand and for automatic grasp synthesis in virtual environments.     

Static grip selection for robot-based automated assembly systems

In this article we present an algorithmic approach to determine the suitable grasp of an object in an automated assembly environment. The algorithm is based on the available object surfaces and the initial and final task constraints and gripper characteristics. If the imposed task and gripper constraints do not allow a possible grasp, intermediate motions may need to be made to reorient the part. Once a set of possible grasps which statisfy task and gripper constraints are found, the stability of each grasp is analyzed using screw theory. An optimal grasp is one which minimizes the grasping forces over the possible set of grasps. Results utilizing our methodology are presented. Our method can be interfaced with CAD database such as a solid modelling system based on boundary representation for automatic selection of grasping configurations.     

Synthesizing two-fingered grippers for positioning and identifyingobjects

Most industrial grippers now in use are two-fingered. Among them the parallel-jaw gripper is the simplest. It can partially remove the pose uncertainty of an object through grasping, such as the orientation uncertainty. This paper addresses a new type of grippers with the finger configuration of four circles instead of two parallel lines. It has a number of important advantages. Especially, it achieves form-closure and confines the object to a locally unique pose, so as to remove the pose uncertainty completely. It allows the gripped object to reach this pose freely without loss of required friction in the direction perpendicular to the grasping plane. More information can be acquired for identifying the object and its grasp mode. As a result the identification can be performed at one grasp. The key parameter of a symmetric four-pin gripper is the distance (span) between two pin centers on each finger, which depends upon the object shape and impacts the closure property, Based on a new approach to the grasp geometry, selection and limitations of the span are illustrated.     

基于点云采样权重估计的未知物体抓取位姿生成方法

针对机械臂在非结构环境中对未知物体抓取位姿生成困难及抓取稳定性差的问题,提出一种基于点云采样权重估计的抓取位姿生成方法.首先通过移动深度相机的方式拼接得到较完整的物体点云信息,并对物体的几何特性进行分析,有效避开物体不宜抓取的位置进行抓取位姿样本生成;然后结合几何约束条件实现抓取位姿搜索,并利用力封闭条件对样本稳定性进行评估;最后为了对实际的抓取位姿进行评价,根据其稳定性、夹取深度、夹取角度等设定抓取可行性指标,据此在工作空间输出最佳抓取位姿并完成指定的抓取任务.实验结果表明,采用所提方法能够高效生成大量且稳定的抓取位姿,并在仿真环境中有效实现机械臂对单个或多个随机摆放的未知物体的抓取任务.     

Kinematic analysis and planning for form closure grasps by robotic hands

A new approach to the planning of form closure grasps by robotic hands is presented. The form closure grasp is to constrain a rigid workpiece by surrounding the object surface with mechanical fingers so that the object motion is geometrically constrained in all directions. First, kinematic conditions for the form closure grasp are obtained for workpieces with smooth surfaces, and are extended those one comprising edges and vertices. An efficient algorithm for examining the form closure grasp conditions is then developed by applying linear programming techniques. A computer-assisted planning system is also developed for the synthesis of grasping points as well as for the design of grippers. Fingertip locations are determined so as to accomplish the form closure grasp, given the geometry of a workpiece. A couple of examples demonstrate the usefulness of the method.     

Classification of grasps by robot hands

Grasping robotic hands is classified into three categories based on the object connectivity. We decompose the space of contact forces into four subspaces and develop a method to determine the dimensions of the subspaces with respect to the connectivity of the grasped object. The relationships we obtain reveal the kinematic and static characteristics of three categories of grasps. It indicates how contact forces can be decomposed corresponding to each type of grasp. The technique also provides a guideline for determining the distribution of contact forces on grasped objects. We analyze how power grasps are identified from the object connectivity and used to synthesize hand configurations for grasping and manipulation tasks. A physical interpretation of the subspaces and the determination of their dimensions are illustrated by examples.     

Robust object manipulation for tactile-based blind grasping

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 neglected in related work. Simulation and hardware results validate the effectiveness of the proposed approach.     

Enhanced continuous valued Q-learning for real autonomous robots

A parallel-jaw gripper is a very useful tool for robot manipulation tasks due to its simple mechanism and control. This fact limits the range of successful grasps it can undergo, and also makes it unfeasible under uncertainties. Thus, it is desirable to improve its dexterity and manipulability. In this paper, we propose a new design of a two-fingered parallel gripper that utilizes rolling at the contacts for object repositioning and reorientation, aimed at effective firm grasps. We name it the scrollic gripper, an acronym for synchronously closing with rolling constraints. At first, the background to utilize the rolling constraints is described. Then, grasping and manipulation of the gripper are discussed. In grasp acquisition, we propose a quality function for evaluating grasp stability. The sophisticated hardware and functioning for the scrollic gripper consist, basically, on implementation of an additional degree-of-freedom to the conventional parallel-jaw gripper, leading to grasp acquisition and secure grasping.     

Multi-fingered grasp synthesis based on the object dynamic properties

The grasping and manipulation of objects, especially when they are heavy with respect to the hand power capability, requires the synthesis of grasp configurations that explicitly take into account the dynamic properties of the object. Specifically, suitable grasp configurations reducing gravitational and inertial effects during object manipulation, and minimizing and equally distributing the grasping forces among all the available fingers, must be computed. A new method for fast synthesis of multi-fingered grasp configurations is proposed in this paper. In particular, to reduce the computational complexity, all the regions of the object surface favoring the synthesis of minimal inertia grasps are evaluated first. Then, a reduced number of discrete grasping regions are selected on the basis of the fingertip size, model uncertainty, and surface curvature. Finally, an exhaustive search of the optimal grasp configurations with respect to the grasp quality is performed. Several case studies and comparisons with other methods are proposed to demonstrate the effectiveness of the proposed approach.     

EC-EGI: enriched complex EGI for 3D shape registration

Extended Gaussian image (EGI) and complex EGI (CEGI) have been widely used as the representation of 3D shapes for shape recognition and pose estimation. In this work, we extend the representations and present a new representation named enriched complex extended Gaussian image (EC-EGI). The representation follows the same framework of EGI and CEGI, which is to represent each surface patch of the target 3D shape as a weight at the associated spot on the surface of the Gaussian sphere. However, while the original CEGI uses a single complex number as the weight, the new representation uses three complex numbers, which are related to the centroid position of the surface patch in 3D. With the inclusion of more information in the new representation, not only could object pose be determined more accurately, but also some key ambiguities of shape representation that CEGI and EGI have also removed. The translation parameters in the pose estimation application could also be determined in a simpler and more accurate way. In addition, the Gaussian sphere partition problem of CEGI is no longer present. Experimental results on synthetic and real image data are shown to illustrate the performance of the proposed representation in pose estimation.     

A New Methodology for Human Grasp Prediction

Grasping is an essential requirement for digital human models (DHMs). It is a complex process and thus a challenging problem for DHMs, involving a skeletal structure with many degrees-of-freedom (DOFs), cognition, and interaction between the human and objects in the environment. Furthermore, grasp planning involves not only finding the shape of the hand and the position and orientation of the wrist but also the posture of the upper body required for producing realistic grasping simulations. In this paper, a new methodology is developed for grasping prediction by combining a shape-matching method and an optimization-based posture prediction technique. We use shape matching to pick a hand shape from a database of stored grasps, then position the hand around the object. The posture prediction algorithm then calculates the optimal posture for the whole upper body necessary to execute the grasp. The proposed algorithm is tested on a variety of objects in a 3-D environment. The results are realistic and suggest that the new method is more suitable for grasp planning than conventional methods. This improved performance is particularly apparent when the nature of the grasped objects is not known a priori , and when a complex high-DOF hand model is necessary.      

On the generation of a variety of grasps

In everyday life, people use a large diversity of hand configurations while reaching out to grasp an object. They tend to vary their hands position/orientation around the object and their fingers placement on its surface according to the object properties such as its weight, shape, friction coefficient and the task they need to accomplish. Taking into account these properties, we propose a method for generating such a variety of good grasps that can be used for the accomplishment of many different tasks. Grasp synthesis is formulated as a single constrained optimization problem, generating grasps that are feasible for the hand’s kinematics by minimizing the norm of the joint torque vector of the hand ensuring grasp stability. Given an object and a kinematic hand model, this method can easily be used to build a library of the corresponding object possible grasps. We show that the approach is adapted to different representations of the object surface and different hand kinematic models.