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Same Object, Different Grasps: Data and Semantic Knowledge for Task-Oriented Grasping [article]

Adithyavairavan Murali, Weiyu Liu, Kenneth Marino, Sonia Chernova, Abhinav Gupta
2020 arXiv   pre-print
The dataset contains 250K task-oriented grasps for 56 tasks and 191 objects along with their RGB-D information.  ...  We take advantage of this new breadth and diversity in the data and present the GCNGrasp framework which uses the semantic knowledge of objects and tasks encoded in a knowledge graph to generalize to new  ...  [19] and Ardón et al. [20] , we reason about semantic knowledge for task-oriented grasping, but we leverage semantic knowledge for generalization to novel object classes and tasks.  ... 
arXiv:2011.06431v2 fatcat:52zg6zdzxrapjbxod5j6tlfkbm

A horse of a different colour: Do patients with semantic dementia recognise different versions of the same object as the same?

M. Ikeda, K. Patterson, K.S. Graham, M.A. Lambon Ralph, J.R. Hodges
2006 Neuropsychologia  
Ten patients with semantic dementia resulting from bilateral anterior temporal lobe atrophy, and 10 matched controls, were tested on an object recognition task in which they were invited to choose (from  ...  a four-item array) the picture representing "the same thing" as an object picture that they had just inspected and attempted to name.  ...  Rhys Davies and Dr. Anna Woollams for help with the MRI pictures and other illustrations for this paper.  ... 
doi:10.1016/j.neuropsychologia.2005.07.006 pmid:16115656 fatcat:evyxye6oq5edfimvh5rodzpaby

Semantic grasping: planning task-specific stable robotic grasps

Hao Dang, Peter K. Allen
2014 Autonomous Robots  
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.  ...  For the next step, we will be considering possible ways to generalize the semantic affordance map so that it would be easier to transfer grasping knowledge between objects and tasks while preserving their  ... 
doi:10.1007/s10514-014-9391-2 fatcat:bdazzjdkdrd2flle33ckr6stpm

Semantic grasping: Planning robotic grasps functionally suitable for an object manipulation task

Hao Dang, Peter K. Allen
2012 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems  
We design an example based planning framework to generate semantic grasps, stable grasps that are functionally suitable for specific object manipulation tasks.  ...  We introduce a semantic affordance map, which relates local geometry to a set of predefined semantic grasps that are appropriate to different tasks.  ...  ACKNOWLEDGEMENTS The authors would like to thank Jonathan Weisz and Lixing Dong in the pursuit of this work.  ... 
doi:10.1109/iros.2012.6385563 dblp:conf/iros/DangA12 fatcat:zzu5skwa6bgqtgalkytry3nase

Generating Task-specific Robotic Grasps [article]

Mark Robson, Mohan Sridharan
2022 arXiv   pre-print
This paper describes a method for generating robot grasps by jointly considering stability and other task and object-specific constraints.  ...  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  ...  Rows 1-5 show use of same task-specific model (of screwdriver class) for the same task (tool use) across different object classes.  ... 
arXiv:2203.10498v1 fatcat:o42v3eukmbdrzgkmjhdkoopbny

Grasp representations depend on knowledge and attention

Kao-Wei Chua, Daniel N. Bub, Michael E. J. Masson, Isabel Gauthier
2018 Journal of Experimental Psychology. Learning, Memory and Cognition  
We find that that familiarity with objects has a facilitative effect on grasping actions, with knowledge about the object's canonical orientation or its name speeding grasping actions for familiar objects  ...  with data collection for these projects.  ...  By contrast, the use task elicited nonfunctional grasps for the same objects.  ... 
doi:10.1037/xlm0000453 pmid:28933905 fatcat:a2re4zcftbad5n5nls6qt225jq

End-to-End Learning of Semantic Grasping [article]

Eric Jang, Sudheendra Vijayanarasimhan, Peter Pastor, Julian Ibarz, Sergey Levine
2017 arXiv   pre-print
We consider the task of semantic robotic grasping, in which a robot picks up an object of a user-specified class using only monocular images.  ...  Inspired by the two-stream hypothesis of visual reasoning, we present a semantic grasping framework that learns object detection, classification, and grasp planning in an end-to-end fashion.  ...  Acknowledgments We would like to thank Vincent Vanhoucke for organization and discussion, John-Michael Burke for assistance with data collection and robot hardware, Zbigniew Wojna for helpful advice on  ... 
arXiv:1707.01932v3 fatcat:iota2s7m65bsjauolzxqqrlc5e

Vision-based Robotic Grasp Detection From Object Localization, Object Pose Estimation To Grasp Estimation: A Review [article]

Guoguang Du, Kai Wang, Shiguo Lian, Kaiyong Zhao
2020 arXiv   pre-print
This task provides the regions of the target object in the input data.  ...  These three subtasks could accomplish the robotic grasping task with different combinations.  ...  ., 2019g] learns semantic-aware point-level instance embedding and semantic features of the points belonging to the same instance are fused together to make per-point semantic predictions.  ... 
arXiv:1905.06658v2 fatcat:6u3k2ltwifaanjpp2nkayyj2f4

A Deep Learning Approach to Grasping the Invisible [article]

Yang Yang, Hengyue Liang, Changhyun Choi
2020 arXiv   pre-print
In this problem, pushes are needed to search for the target and rearrange cluttered objects around it to enable effective grasps.  ...  We study an emerging problem named "grasping the invisible" in robotic manipulation, in which a robot is tasked to grasp an initially invisible target object via a sequence of pushing and grasping actions  ...  Data Collection and Training We collect the data with the following procedure: n target candidates (i.e., detectable by the semantic segmentation module) and m basic objects are randomly selected and dropped  ... 
arXiv:1909.04840v2 fatcat:bq32bqu5jrgrnn7x6byhq3dycq

GKNet: grasp keypoint network for grasp candidates detection [article]

Ruinian Xu, Fu-Jen Chu, Patricio A. Vela
2021 arXiv   pre-print
Contemporary grasp detection approaches employ deep learning to achieve robustness to sensor and object model uncertainty.  ...  A final filtering strategy based on discrete and continuous orientation prediction removes false correspondences and further improves grasp detection performance.  ...  Roughly speaking, object grasping requires resolving the what, where, and how. GKNet targets the how part.  ... 
arXiv:2106.08497v3 fatcat:453hdniyanbyborxg66inxxrc4

Robot Grasping Based on Stacked Object Classification Network and Grasping Order Planning

Chenlu Liu, Di Jiang, Weiyang Lin, Luis Gomes
2022 Electronics  
In this paper, the robot grasping for stacked objects is studied based on object detection and grasping order planning.  ...  At the same time, SOCN adopts the transformer architecture and has a self-attention mechanism for feature learning.  ...  In [17] , the author constructed a conditional random field to model the semantic contents in stacking object regions, and it helps the robot achieve a 69.4% success rate for task-oriented grasping.  ... 
doi:10.3390/electronics11050706 fatcat:zwu7eo6rwnctle2bxv2qlollei

Real-World Semantic Grasp Detection Based on Attention Mechanism [article]

Mingshuai Dong, Shimin Wei, Jianqin Yin, Xiuli Yu
2022 arXiv   pre-print
And we also design a target feature attention mechanism to guide the model focus on the features of target object ontology for grasp prediction according to the semantic information.  ...  In this paper, we propose an end-to-end semantic grasp detection model, which can accomplish both semantic recognition and grasp detection.  ...  anchor with an orientation anchor to enrich further the prior knowledge of grasping position.  ... 
arXiv:2111.10522v2 fatcat:5qpnsydrn5egvajlvedx7g2tmy

Orientation Attentive Robotic Grasp Synthesis with Augmented Grasp Map Representation [article]

Georgia Chalvatzaki, Nikolaos Gkanatsios, Petros Maragos, Jan Peters
2021 arXiv   pre-print
Existing grasp generation approaches are cursed to construct discontinuous grasp maps by aggregating annotations for drastically different orientations per grasping point.  ...  Inherent morphological characteristics in objects may offer a wide range of plausible grasping orientations that obfuscates the visual learning of robotic grasping.  ...  One remaining issue is the multiple instances of the same grasp centers and angles using different jaw sizes.  ... 
arXiv:2006.05123v2 fatcat:77fgo6orqfdcxou4nu3gchcaze

A Survey: Robot Grasping [article]

Kenechi Dukor, Tejumade Afonja
2021 Zenodo  
when executing a grasping task.  ...  In this survey paper, we focus on robot grasping, which is a significant challenge for robots and hinders their successful deployment in the real world.  ...  Model-based In this approach, specific physical and geometric knowledge about the object is used to solve the grasping task.  ... 
doi:10.5281/zenodo.5559125 fatcat:zw4zokcjzzchhc6dqvlg5ftohq

TMS over the supramarginal gyrus delays selection of appropriate grasp orientation during reaching and grasping tools for use

Tomás McDowell, Nicholas P. Holmes, Alan Sunderland, Martin Schürmann
2018 Cortex  
Our findings implicate a bilateral role of the SMG in correcting movements and selection of appropriate grasp orientation during reaching to grasp tools for use.  ...  To study the integration of manipulation control and tool knowledge within a narrow time window we mechanically perturbed the orientation of the tool to force participants to correct grasp orientation  ...  Grasping an object to be acted with requires knowledge of appropriate orientation in relation to the hand.  ... 
doi:10.1016/j.cortex.2018.03.002 pmid:29609118 fatcat:awpofb2j5fd4xancegs3ynuz6q
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