Grasp moduli spaces and spherical harmonics

Pokorny, Florian T.; Bekiroglu, Yasemin; Kragić, Danica

doi:10.1109/icra.2014.6906886

Cited by 14 publications

(7 citation statements)

References 20 publications

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“…Recently, Pokorny et al in [18,19] present an infinite dimensional space-the Grasp Moduli Space, which combines grasps and objects parametrized by smooth differentiable functions. In this space various surface/grasp configurations can be continuously deformed in order to synthesize force closed grasps on novel objects.…”

Section: Related Workmentioning

confidence: 99%

Category-based task specific grasping

Nikandrova

Kyrki

2015

Robotics and Autonomous Systems

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h i g h l i g h t s• A probabilistic approach for task-specific category based grasping is proposed.• The grasp stability is maximized probabilistically over shape uncertainty.• The approach integrates information over all training objects for better generalization.• The technique can cope with a sparser training set than most data-driven methods.• Only incomplete point clouds obtained from a single RGB-D image are needed. a b s t r a c tThe problem of finding stable grasps has been widely studied in robotics. However, in many applications the resulting grasps should not only be stable but also applicable for a particular task. Task-specific grasps are closely linked to object categories so that objects in a same category can be often used to perform the same task. This paper presents a probabilistic approach for task-specific stable grasping of objects with shape variations inside the category. An optimal grasp is found as a grasp that is maximally likely to be task compatible and stable taking into account shape uncertainty in a probabilistic context. The method requires only partial models of new objects for grasp generation and only few models and example grasps are used during the training stage. The experiments show that the approach can use multiple models to generalize to new objects in that it outperforms grasping based on the closest model. The method is shown to generate stable grasps for new objects belonging to the same class as well as for similar in shape objects of different categories.

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Section: Related Workmentioning

confidence: 99%

Category-based task specific grasping

Nikandrova

Kyrki

2015

Robotics and Autonomous Systems

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“…In addition, making use of local symmetry properties of objects has been shown to capture key shape features and generate heuristics based grasp candidates [18], which however requires full observation of objects. Shape modelling, where objects are parameterized using smooth differentiable functions from point clouds via a spectral analysis [19] has been employed to represent objects and grasps jointly in a common space allowing for transferring grasps on various objects. However this smooth parametrization can deteriorate with partial point cloud data, the shape space needs to accommodate missing data while avoiding unrealistic shape reconstructions.…”

Section: Related Workmentioning

confidence: 99%

Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects

Farias

Marturi

Bekiroglu

2021

IEEE Robot. Autom. Lett.

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This paper addresses the problem of simultaneously exploring an unknown object to model its shape, using tactile sensors on robotic fingers, while also improving finger placement to optimise grasp stability. In many situations, a robot will have only a partial camera view of the near side of an observed object, for which the far side remains occluded. We show how an initial grasp attempt, based on an initial guess of the overall object shape, yields tactile glances of the far side of the object which enable the shape estimate and consequently the successive grasps to be improved. We propose a grasp exploration approach using a probabilistic representation of shape, based on Gaussian Process Implicit Surfaces. This representation enables initial partial vision data to be augmented with additional data from successive tactile glances. This is combined with a probabilistic estimate of grasp quality to refine grasp configurations. When choosing the next set of finger placements, a bi-objective optimisation method is used to mutually maximise grasp quality and improve shape representation during successive grasp attempts. Experimental results show that the proposed approach yields stable grasp configurations more efficiently than a baseline method, while also yielding improved shape estimate of the grasped object.

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“…Early grasping detection works are mainly based on traditional methods, such as serach algorithm. However, these algorithms cannot work effectively in complex real scenarios [1]. In recent years, deep learning-based methods have achieved excellent results in robotic grasping detection.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Convolutional Neural Network with Gaussian-based Grasping Representation for Robotic Grasping Detection

Cao¹,

Chen²,

Li³

et al. 2021

Preprint

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The method of deep learning has achieved excellent results in improving the performance of robotic grasping detection. However, the deep learning methods used in general object detection are not suitable for robotic grasping detection. Current modern object detectors are difficult to strike a balance between high accuracy and fast inference speed. In this paper, we present an efficient and robust fully convolutional neural network model to perform robotic grasping pose estimation from n-channel input image of the real grasping scene. The proposed network is a lightweight generative architecture for grasping detection in one stage. Specifically, a grasping representation based on Guassian kernel is introduced to encode training samples, which embodies the principle of maximum central point grasping confidence. Meanwhile, to extract multiscale information and enhance the feature discriminability, a receptive field block (RFB) is assembled to the bottleneck of our grasping detection architecture. Besides, pixel attention and channel attention are combined to automatically learn to focus on fusing context information of varying shapes and sizes by suppressing the noise feature and highlighting the grasping object feature. Extensive experiments on two public grasping datasets, Cornell and Jacquard demonstrate the state-of-the-art performance of our method in balancing accuracy and inference speed. The network is an order of magnitude smaller than other excellent algorithms, while achieving better performance with accuracy of 98.9% and 95.6% on the Cornell and Jacquard datasets, respectively.

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Grasp moduli spaces and spherical harmonics

Cited by 14 publications

References 20 publications

Category-based task specific grasping

Category-based task specific grasping

Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects

Lightweight Convolutional Neural Network with Gaussian-based Grasping Representation for Robotic Grasping Detection

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