Generalizing grasps across partly similar objects

IEEE Transactions on Robotics, 2000

We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally, for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.

Robotics and Autonomous Systems, 2014

Humans excel when dealing with everyday manipulation tasks, being able to learn new skills, and to adapt to different complex environments. This results from a lifelong learning, and also observation of other skilled humans. To obtain similar dexterity with robotic hands, cognitive capacity is needed to deal with uncertainty. By extracting relevant multisensor information from the environment (objects), knowledge from previous grasping tasks can be generalized to be applied within different contexts. Based on this strategy, we show in this paper that learning from human experiences is a way to accomplish our goal of robot grasp synthesis for unknown objects. In this article we address an artificial system that relies on knowledge from previous human object grasping demonstrations. A learning process is adopted to quantify probabilistic distributions and uncertainty. These distributions are combined with preliminary knowledge towards inference of proper grasps given a point cloud of an unknown object. In this article, we designed a method that comprises a twofold process: object decomposition and grasp synthesis. The decomposition of objects into primitives is used, across which similarities between past observations and new unknown objects can be made. The grasps are associated with the defined object primitives, so that feasible object regions for grasping can be determined. The hand pose relative to the object is computed for the pre-grasp and the selected grasp. We have validated our approach on a real robotic platform-a dexterous robotic hand. Results show that the segmentation of the object into primitives allows to identify the most suitable regions for grasping based on previous learning. The proposed approach provides suitable grasps, better than more time consuming analytical and geometrical approaches, contributing for autonomous grasping.

Generalising dexterous grasps to novel objects is an open problem. We show how to learn grasps for high DoF hands that generalise to novel objects, given as little as one demonstrated grasp. During grasp learning two types of probability density are learned that model the demonstrated grasp. The first density type (the contact model) models the relationship of an individual finger part to local surface features at its contact point. The second density type (the hand configuration model) models the whole hand configuration during the approach to grasp. When presented with a new object, many candidate grasps are generated, and a kinematically feasible grasp is selected that maximises the product of these densities. We demonstrate 31 successful grasps on novel objects (an 86% success rate), transferred from 16 training grasps. The method enables: transfer of dexterous grasps within object categories; across object categories; to and from objects where there is no complete model of the object available; and using two different dexterous hands.

IEEE Transactions on Robotics, 2014

We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally, for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.

2014

Generalising dexterous grasps to novel objects is an open problem. We show how to learn grasps for high DoF hands that generalise to novel objects, given as little as one demonstrated grasp. During grasp learning two types of probability density are learned that model the demonstrated grasp. The first density type (the contact model) models the relationship of an individual finger part to local surface features at its contact point. The second density type (the hand configuration model) models the whole hand configuration during the approach to grasp. When presented with a new object, many candidate grasps are generated, and a kinematically feasible grasp is selected that maximises the product of these densities. We demonstrate 31 successful grasps on novel objects (an 86% success rate), transferred from 16 training grasps. The method enables: transfer of dexterous grasps within object categories; across object categories; to and from objects where there is no complete model of the object available; and using two different dexterous hands.

arXiv (Cornell University), 2022

This paper describes a method for generating robot grasps by jointly considering stability and other task and object-specific constraints. We introduce a three-level representation that is acquired for each object class from a small number of exemplars of objects, tasks, and relevant grasps. The representation encodes task-specific knowledge for each object class as a relationship between a keypoint skeleton and suitable grasp points that is preserved despite intra-class variations in scale and orientation. The learned models are queried at run time by a simple sampling-based method to guide the generation of grasps that balance task and stability constraints. We ground and evaluate our method in the context of a Franka Emika Panda robot assisting a human in picking tabletop objects for which the robot does not have prior CAD models. Experimental results demonstrate that in comparison with a baseline method that only focuses on stability, our method is able to provide suitable grasps for different tasks.

International Journal of Advanced Robotic Systems, 2011

2010

This paper deals with the problem of estimating an appropriate hand posture to grasp an object, from 2D object's visual cues in a many-to-many (objects,grasp) configuration. A statistical learning protocol implementing vector-valued regression is adopted for both classifying the most likely grasp type and estimating the hand posture. An extensive experimental evaluation on a publicly available dataset of visuo-motor data reports very promising results and encourages further investigations.

Paladyn, Journal of Behavioral Robotics, 2015

Action affordance learning based on visual sensory information is a crucial problem within the development of cognitive agents. In this paper, we present a method for learning action affordances based on basic visual features, which can vary in their granularity, order of combination and semantic content. The method is provided with a large and structured set of visual features, motivated by the visual hierarchy in primates and finds relevant feature action associations automatically. We apply our method in a simulated environment on three different object sets for the case of grasp affordance learning. For box objects,we achieve a 0.90 success probability, 0.80 for round objects and up to 0.75 for open objects, when presented with novel objects. In thiswork,we demonstrate, in particular, the effect of choosing appropriate feature representations. We demonstrate a significant performance improvement by increasing the complexity of the perceptual representation. By that, we present i...

… and Learning, 2009. …, 2009

This paper addresses the issue of learning and representing object grasp affordances, i.e. object-gripper relative configurations that lead to successful grasps. The purpose of grasp affordances is to organize and store the whole knowledge that an agent has about the grasping of an object, in order to facilitate reasoning on grasping solutions and their achievability. The affordance representation consists in a continuous probability density function defined on the 6D gripper pose space -3D position and orientation -, within an object-relative reference frame. Grasp affordances are initially learned from various sources, e.g. from imitation or from visual cues, leading to grasp hypothesis densities. Grasp densities are attached to a learned 3D visual object model, and pose estimation of the visual model allows a robotic agent to execute samples from a grasp hypothesis density under various object poses. Grasp outcomes are used to learn grasp empirical densities, i.e. grasps that have been confirmed through experience. We show the result of learning grasp hypothesis densities from both imitation and visual cues, and present grasp empirical densities learned from physical experience by a robot.