Open AccessProceedings Article
Grasping familiar objects using shape context
Jeannette Bohg,Danica Kragic +1 more
- pp 1-6
TLDR
In this article, the global contour of an object is extracted from a monocular image and a suitable grasp is generated using a learning framework where prototypical grasping points are learned from several examples and then used on novel objects.Abstract:
We present work on vision based robotic grasping. The proposed method relies on extracting and representing the global contour of an object in a monocular image. A suitable grasp is then generated using a learning framework where prototypical grasping points are learned from several examples and then used on novel objects. For representation purposes, we apply the concept of shape context and for learning we use a supervised learning approach in which the classifier is trained with labeled synthetic images. Our results show that a combination of a descriptor based on shape context with a non-linear classification algorithm leads to a stable detection of grasping points for a variety of objects. Furthermore, we will show how our representation supports the inference of a full grasp configuration.read more
Citations
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Visual object-action recognition: Inferring object affordances from human demonstration
TL;DR: A method is presented for categorizing manipulated objects and human manipulation actions in context of each other, able to simultaneously segment and classify human hand actions, and detect and classify the objects involved in the action.
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Affordance detection of tool parts from geometric features
TL;DR: This work proposes two approaches for learning affordances from local shape and geometry primitives: superpixel based hierarchical matching pursuit (S-HMP); and structured random forests (SRF), and introduces a large RGB-Depth dataset where tool parts are labeled with multiple affordances and their relative rankings.
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Assessing Grasp Stability Based on Learning and Haptic Data
TL;DR: A probabilistic learning framework to assess grasp stability is proposed and it is shown that knowledge about grasp stability can be inferred using information from tactile sensors, which opens a number of interesting venues for the future research.
Proceedings ArticleDOI
Detecting object affordances with Convolutional Neural Networks
TL;DR: This work presents a novel and real-time method to detect object affordances from RGB-D images that trains a deep Convolutional Neural Network to learn deep features from the input data in an end-to-end manner.
Journal ArticleDOI
Learning of grasp selection based on shape-templates
Alexander Herzog,Peter Pastor,Mrinal Kalakrishnan,Ludovic Righetti,Jeannette Bohg,Tamim Asfour,Stefan Schaal +6 more
TL;DR: An algorithm based on the assumption that similarly shaped objects can be grasped in a similar way is introduced, able to synthesize good grasp poses for unknown objects by finding the best matching object shape templates associated with previously demonstrated grasps.
References
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Journal ArticleDOI
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Proceedings ArticleDOI
Automatic grasp planning using shape primitives
TL;DR: This paper aims to simplify automatic grasp planning for robotic hands by modeling an object as a set of shape primitives, such as spheres, cylinders, cones and boxes, to generate aSet of grasp starting positions and pregrasp shapes that can then be tested on the object model.
Journal ArticleDOI
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