Data-Driven Grasp Synthesis - A Survey <s> A. Analytic vs. Data-Driven Approaches <s> This paper presents fast and simple algorithms for directly constructing force-closure grasps based on the shape of the grasped object. The synthesis of force-closure grasps finds in dependent regions of contact for the fingertips, such that the motion of the grasped object is totally constrained. A force- closure grasp implies equilibrium grasps exist. In the reverse direction, we show that most nonmarginal equilibrium grasps are force-closure grasps. <s> BIB001 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Analytic vs. Data-Driven Approaches <s> The authors address the problem of planning optimal grasps. Two general optimality criteria that consider the total finger force and the maximum finger force are introduced and discussed. Their formalization using various metrics on a space of generalized forces is detailed. The geometric interpretation of the two criteria leads to an efficient planning algorithm. An example of its use in a robotic environment equipped with two-jaw and three-jaw is described. > <s> BIB002 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Analytic vs. Data-Driven Approaches <s> This article presents a survey of the existing computational algorithms meant for achieving four important properties in autonomous multifingered robotic hands. The four properties are: dexterity, equilibrium, stability, and dynamic behavior The multifingered robotic hands must be controlled so as to possess these properties and hence be able to autonomously perform complex tasks in a way similar to human hands. Existing algorithms to achieve dexterity primarily involve solving an unconstrained linear programming problem where an objective function can be chosen to represent one or more of the currently known dexterity measures. Algorithms to achieve equilibrium also constitute solving a linear program ming problem wherein the positivity, friction, and joint torque constraints of all fingers are accounted for while optimizing the internal grasping forces. Stability algorithms aim at achiev ing positive definite grasp impedance matrices by solving for the required fingertip impedances. This problem reduces to a nonlinear programming problem. Dynamic behavior algorithms determine fingertip impedances, which, when achieved, lead to a desired dynamic behavior. This problem, too, becomes a linear programming problem. If a robotic hand has to acquire any or all of these proper ties, the corresponding algorithms should become integral parts of the hand control system. These algorithms are collectively referred to in this article as robot grasp synthesis algorithms. <s> BIB003 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Analytic vs. Data-Driven Approaches <s> One of the challenges in developing real-world autonomous robots is the need for integrating and rigorously testing high-level scripting, motion planning, perception, and control algorithms. For this purpose, we introduce an open-source cross-platform software architecture called OpenRAVE, the Open Robotics and Animation Virtual Environment. OpenRAVE is targeted for real-world autonomous robot applications, and includes a seamless integration of 3-D simulation, visualization, planning, scripting and control. A plugin architecture allows users to easily write custom controllers or extend functionality. With OpenRAVE plugins, any planning algorithm, robot controller, or sensing subsystem can be distributed and dynamically loaded at run-time, which frees developers from struggling with monolithic code-bases. Users of OpenRAVE can concentrate on the development of planning and scripting aspects of a problem without having to explicitly manage the details of robot kinematics and dynamics, collision detection, world updates, and robot control. The OpenRAVE architecture provides a flexible interface that can be used in conjunction with other popular robotics packages such as Player and ROS because it is focused on autonomous motion planning and high-level scripting rather than low-level control and message protocols. OpenRAVE also supports a powerful network scripting environment which makes it simple to control and monitor robots and change execution flow during run-time. One of the key advantages of open component architectures is that they enable the robotics research community to easily share and compare algorithms. <s> BIB004 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Analytic vs. Data-Driven Approaches <s> Precision grasp synthesis has received a lot of attention in past few last years. However, real mechanical hands can hardly assure that the fingers will precisely touch the object at the computed contact points. The concept of independent contact regions (ICRs) was introduced to provide robustness to finger positioning errors during an object grasping: A finger contact anywhere inside each of these regions assures a force-closure grasp, despite the exact contact position. This paper presents an efficient algorithm to compute ICRs with any number of frictionless or frictional contacts on the surface of any 3-D object. The proposed approach generates the independent regions by growing them around the contact points of a given starting grasp. A two-phase approach is provided to find a locally optimal force-closure grasp that serves as the starting grasp, considering as grasp quality measure the largest perturbation wrench that the grasp can resist, independently of the perturbation direction. The proposed method can also be applied to compute ICRs when several contacts are fixed beforehand. The approach has been implemented, and application examples are included to illustrate its performance. <s> BIB005 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Analytic vs. Data-Driven Approaches <s> This paper addresses the problem of robot grasping in conditions of uncertainty. We propose a grasp controller that deals robustly with this uncertainty using feedback from different contact-based sensors. This controller assumes a description of grasp consisting of a primitive that only determines the initial configuration of the hand and the control law to be used. We exhaustively validate the controller by carrying out a large number of tests with different degrees of inaccuracy in the pose of the target objects and by comparing it with results of a naive grasp controller. <s> BIB006 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Analytic vs. Data-Driven Approaches <s> We propose a system for improving grasping using fingertip optical proximity sensors that allows us to perform online grasp adjustments to an initial grasp point without requiring premature object contact or regrasping strategies. We present novel optical proximity sensors that fit inside the fingertips of a Barrett Hand, and demonstrate their use alongside a probabilistic model for robustly combining sensor readings and a hierarchical reactive controller for improving grasps online. This system can be used to complement existing grasp planning algorithms, or be used in more interactive settings where a human indicates the location of objects. Finally, we perform a series of experiments using a Barrett hand equipped with our sensors to grasp a variety of common objects with mixed geometries and surface textures. <s> BIB007 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Analytic vs. Data-Driven Approaches <s> Since the introduction of independent contact regions in order to compensate for shortcomings in the positioning accuracy of robotic hands, alternative methods for their generation have been proposed. Due to the fact that (in general) such regions are not unique, the computation methods used usually reflect the envisioned application and/or underlying assumptions made. This paper introduces a parallelizable algorithm for the efficient computation of independent contact regions, under the assumption that a user input in the form of initial guess for the grasping points is readily available. The proposed approach works on discretized 3D-objects with any number of contacts and can be used with any of the following models: frictionless point contact, point contact with friction and soft finger contact. An example of the computation of independent contact regions comprising a non-trivial task wrench space is given. <s> BIB008 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Analytic vs. Data-Driven Approaches <s> Robotic grasping in unstructured environments requires the ability to select grasps for unknown objects and execute them while dealing with uncertainty due to sensor noise or calibration errors. In this work, we propose a simple but robust approach to grasp selection for unknown objects, and a reactive adjustment approach to deal with uncertainty in object location and shape. The grasp selection method uses 3D sensor data directly to determine a ranked set of grasps for objects in a scene, using heuristics based on both the overall shape of the object and its local features. The reactive grasping approach uses tactile feedback from fingertip sensors to execute a compliant robust grasp. We present experimental results to validate our approach by grasping a wide range of unknown objects. Our results show that reactive grasping can correct for a fair amount of uncertainty in the measured position or shape of the objects, and that our grasp selection approach is successful in grasping objects with a variety of shapes. <s> BIB009 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Analytic vs. Data-Driven Approaches <s> Personal robots can only become widespread if they are capable of safely operating among humans. In uncertain and highly dynamic environments such as human households, robots need to be able to instantly adapt their behavior to unforseen events. In this paper, we propose a general framework to achieve very contact-reactive motions for robotic grasping and manipulation. Associating stereotypical movements to particular tasks enables our system to use previous sensor experiences as a predictive model for subsequent task executions. We use dynamical systems, named Dynamic Movement Primitives (DMPs), to learn goal-directed behaviors from demonstration. We exploit their dynamic properties by coupling them with the measured and predicted sensor traces. This feedback loop allows for online adaptation of the movement plan. Our system can create a rich set of possible motions that account for external perturbations and perception uncertainty to generate truly robust behaviors. As an example, we present an application to grasping with the WAM robot arm. <s> BIB010 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Analytic vs. Data-Driven Approaches <s> We present a novel robotic grasp controller that allows a sensorized parallel jaw gripper to gently pick up and set down unknown objects once a grasp location has been selected. Our approach is inspired by the control scheme that humans employ for such actions, which is known to centrally depend on tactile sensation rather than vision or proprioception. Our controller processes measurements from the gripper's fingertip pressure arrays and hand-mounted accelerometer in real time to generate robotic tactile signals that are designed to mimic human SA-I, FA-I, and FA-II channels. These signals are combined into tactile event cues that drive the transitions between six discrete states in the grasp controller: Close, Load, Lift and Hold, Replace, Unload, and Open. The controller selects an appropriate initial grasping force, detects when an object is slipping from the grasp, increases the grasp force as needed, and judges when to release an object to set it down. We demonstrate the promise of our approach through implementation on the PR2 robotic platform, including grasp testing on a large number of real-world objects. <s> BIB011 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Analytic vs. Data-Driven Approaches <s> Underactuation in robotic hands is currently attracting a lot of interest from researchers. The challenging idea of underactuation in grasping is that hands, with reduced number of actuators, supported by suitable design and control, may not suffer from reduced performances. This trend is also strengthened by recent neuroscience studies which demonstrates that also humans use sensorimotor synergies to control the hand in performing grasping tasks. In this paper, we focus on the kinematic and force manipulability analyses of underactuated robotic hands. The performances of such hands, regarded as mechanical transformers of inputs as forces and speed into outputs as object wrench and displacements, are assessed by suitably defined manipulation indices. The whole analysis is not limited by rigid-body motion assumptions, but encompasses elastic motions and statically indeterminate configurations by introducing generalized compliance at contacts and actuation. Two examples show the validity of the proposed approach to evaluate underactuated hand performances. <s> BIB012 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Analytic vs. Data-Driven Approaches <s> This work proposes a solution to the grasp synthesis problem, which consist of finding the best hand configuration to grasp a given object for a specific manipulation task while satisfying all the necessary constraints. This problem had been divided into sequential sub-problems, including contact region determination, hand inverse kinematics and force distribution, with the particular constraints of each step tackled independently. This may lead to unnecessary effort, such as when one of the problems has no solution given the output of the previous step as input. To overcome this issue, we present a kinestatic formulation of the grasp synthesis problem that introduces compliance both at the joints and the contacts. This provides a proper framework to synthesize a feasible and prehensile grasp by considering simultaneously the necessary grasping constraints, including contact reachability, object restraint, and force controllability. As a consequence, a solution of the proposed model results in a set of hand configurations that allows to execute the grasp using only a position controller. The approach is illustrated with experiments on a simple planar hand using two fingers and an anthropomorphic robotic hand using three fingers. <s> BIB013 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Analytic vs. Data-Driven Approaches <s> We address the problem of synthesizing planar, bimanual, whole-arm grasps by developing the abstraction of an open chain gripper, an open, planar chain of rigid links and revolute joints contacting a planar, polygonal object, and introducing the concept of a generalized contact. Since two generalized contacts suffice for planar grasps, we leverage previous work on caging and immobilization for two contact grasps to construct an algorithm which synthesizes contact configurations for stable grasping. Simulations show that our methodology can be applied to grasp a wide range of planar objects without relying on special-purpose end-effectors. Representative experiments with the PR2 humanoid robot illustrate that this approach is practical. <s> BIB014 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Analytic vs. Data-Driven Approaches <s> Advanced grasp control algorithms could benefit greatly from accurate tracking of the object as well as an accurate all-around knowledge of the system when the robot attempts a grasp. This motivates our study of the G-SL(AM)2 problem, in which two goals are simultaneously pursued: object tracking relative to the hand and estimation of parameters of the dynamic model. We view the G-SL(AM)2 problem as a filtering problem. Because of stick-slip friction and collisions between the object and hand, suitable dynamic models exhibit strong nonlinearities and jump discontinuities. This fact makes Kalman filters (which assume linearity) and extended Kalman filters (which assume differentiability) inapplicable, and leads us to develop a particle filter. An important practical problem that arises during grasping is occlusion of the view of the object by the robot's hand. To combat the resulting loss of visual tracking fidelity, we designed a particle filter that incorporates tactile sensor data. The filter is evaluated off-line with data gathered in advance from grasp acquisition experiments conducted with a planar test rig. The results show that our particle filter performs quite well, especially during periods of visual occlusion, in which it is much better than the same filter without tactile data. <s> BIB015 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Analytic vs. Data-Driven Approaches <s> We present a novel and simple experimental method called physical human interactive guidance to study human-planned grasping. Instead of studying how the human uses his/her own biological hand or how a human teleoperates a robot hand in a grasping task, the method involves a human interacting physically with a robot arm and hand, carefully moving and guiding the robot into the grasping pose, while the robot's configuration is recorded. Analysis of the grasps from this simple method has produced two interesting results. First, the grasps produced by this method perform better than grasps generated through a state-of-the-art automated grasp planner. Second, this method when combined with a detailed statistical analysis using a variety of grasp measures (physics-based heuristics considered critical for a good grasp) offered insights into how the human grasping method is similar or different from automated grasping synthesis techniques. Specifically, data from the physical human interactive guidance method showed that the human-planned grasping method provides grasps that are similar to grasps from a state-of-the-art automated grasp planner, but differed in one key aspect. The robot wrists were aligned with the object's principal axes in the human-planned grasps (termed low skewness in this paper), while the automated grasps used arbitrary wrist orientation. Preliminary tests show that grasps with low skewness were significantly more robust than grasps with high skewness (77-93%). We conclude with a detailed discussion of how the physical human interactive guidance method relates to existing methods to extract the human principles for physical interaction. <s> BIB016 </s> Data-Driven Grasp Synthesis - A Survey <s> A. Analytic vs. Data-Driven Approaches <s> Grasp quality metrics which analyze the contact wrench space are commonly used to synthesize and analyze preplanned grasps. Preplanned grasping approaches rely on the robustness of stored solutions. Analyzing the robustness of such solutions for large databases of preplanned grasps is a limiting factor for the applicability of data driven approaches to grasping. In this work, we will focus on the stability of the widely used grasp wrench space epsilon quality metric over a large range of poses in simulation. We examine a large number of grasps from the Columbia Grasp Database for the Barrett hand. We find that in most cases the grasp with the most robust force closure with respect to pose error for a particular object is not the grasp with the highest epsilon quality. We demonstrate that grasps can be reranked by an estimate of the stability of their epsilon quality. We find that the grasps ranked best by this method are successful more often in physical experiments than grasps ranked best by the epsilon quality. <s> BIB017
Analytic approaches provide guarantees regarding the criteria that measure the previously mentioned four grasp properties. However, these are usually based on assumptions such as simplified contact models, Coulomb friction and rigid body modeling   . Although these assumptions render grasp analysis practical, inconsistencies and ambiguities especially regarding the analysis of grasp dynamics are usually attributed to their approximate nature. In this context, Bicchi and Kumar  identified the problem of finding an accurate and tractable model of contact compliance as particularly relevant. This is needed to analyze statically-indeterminate grasps in which not all internal forces can be controlled. This case arises e.g. for under-actuated hands or grasp synergies where the number of the controlled degrees of freedom is fewer than the number of contact forces. Prattichizzo et al. BIB012 model such a system by introducing a set of springs at the contacts and joints and show how its dexterity can be analyzed. Rosales et al. BIB013 adopt the same model of compliance to synthesize feasible and prehensile grasps. In this case, only statically-determinate grasps are considered. The problem of finding a suitable hand configuration is cast as a constrained optimization problem in which compliance is introduced to simultaneously address the constraints of contact reachability, object restraint and force controllability. As is the case with many other analytic approaches towards grasp synthesis, the proposed model is only studied in simulation where accurate models of the hand kinematics, the object and their relative alignment are available. In practice, systematic and random errors are inherent to a robotic system and are due to noisy sensors and inaccurate object, robot, etc. models. The relative position of object and hand can therefore only be known approximately which makes an accurate placement of the fingertips difficult. In arXiv:1309.2660v1 [cs.RO] 10 Sep 2013 2000, Bicchi and Kumar  identified a lack of approaches towards synthesizing grasps that are robust to positioning errors. Since then, this problem has shifted into focus. One line of research follows the approach of independent contact regions (ICRs) as defined by Nguyen BIB001 : a set of regions on the object in which each finger can be independently placed anywhere without the grasp loosing the force-closure property. Several examples for computing them are presented by Roa and Suárez BIB005 or Krug et al. BIB008 . Another line of research towards robustness against inaccurate end-effector positioning makes use of the caging formulation. Rodriguez et al.  found that there are caging configurations of a three-fingered manipulator around a planar object that are specifically suited as a way point to grasping it. Once the manipulator is in such configuration, either opening or closing the fingers is guaranteed to result in an equilibrium grasp without the need for accurate positioning of the fingers. Seo et al. BIB014 exploited the fact that two-fingered immobilizing grasps of an object are always preceded by a caging configuration. Full body grasps of planar objects are synthesized by first finding a two-contact caging configuration and then using additional contacts to restrain the object. Results have been presented in simulation and demonstrated on a real robot. Another assumption commonly made in analytic approaches is that precise geometric and physical models of an object are available to the robot which is not always the case. In addition, we may not know the surface properties or friction coefficients, weight, center of mass and weight distribution. Some of these can be retrieved through interaction: Zhang and Trinkle BIB015 propose to use a particle filter to simultaneously estimate the physical parameters of an object and track it while it is being pushed. The dynamic model of the object is formulated as a mixed nonlinear complementarity problem. The authors show that even when the object is occluded and the state estimate cannot be updated through visual observation, the motion of the object is accurately predicted over time. Although methods like this relax some of the assumptions, they are still limited to simulation  BIB013 or consider 2D objects  BIB014 BIB015 . It has been recognized that classical metrics based on analytic formulations, such as the widely used -metric proposed in Ferrari and Canny BIB002 , do not cope well with the challenges arising in unstructured environments. This metric constructs the grasp wrench space (GWS) by computing the convex hull over the wrenches at the contact points between the hand and the object. ranks the quality of a force closure grasp by quantifying the radius of the maximum sphere still fully contained in the GWS. This metric is implemented in simulators such as GraspIt!  and OpenRave BIB004 . Diankov  claims that in practice grasps synthesized using this metric tend to be relatively fragile. Balasubramanian et al. BIB016 systematically tested a number of grasps in the real world that were stable according to classical grasp metrics. Compared to grasps planned by humans and transferred to a robot by kinesthetic teaching on the same objects, they under-performed significantly. A similar study has been conducted by Weisz and Allen BIB017 . It focuses on the ability of the -metric to predict grasp stability under object pose error. The authors found that it performs poorly especially when grasping large objects. As pointed out by Bicchi and Kumar  and Prattichizzo and Trinkle  , grasp closure is often wrongly equated with stability. Closure states the existence of equilibrium which is a necessary but not sufficient condition. Stability can only be defined when considering the grasp as a dynamical system and in the context of its behavior when perturbed from an equilibrium. Seen in this light, the results of the above mentioned studies are not surprising. However, they suggest that there is a large gap between reality and the models for grasping that are currently available and tractable. This gap is one of the reasons for the attention data-driven grasp synthesis has received during the last decade. Contrary to analytic approaches, methods following this paradigm place more weight on the object representation and the perceptual processing, e.g., feature extraction, similarity metrics, object recognition or classification and pose estimation. The resulting data is then used to retrieve grasps from some knowledge base or sample and rank them by comparison to existing grasp experience. The parameterization of the grasp is less specific (e.g. an approach vector instead of fingertip positions) and therefore accommodates for uncertainties in perception and execution. This provides a natural precursor to reactive grasping BIB006 BIB007 BIB010 BIB009 BIB011 , which, given a grasp hypothesis, considers the problem of robustly acquiring it under uncertainty. Data-driven methods cannot provide guarantees regarding the aforementioned properties of dexterity, equilibrium, stability and dynamic behaviour BIB003 . They can only be verified empirically. However, they form the basis for studying grasp dynamics and further developing analytic models that better resemble reality.