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Task-Based Robot Grasp Planning Using Probabilistic Inference
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. (Computer Vision and Active Perception (CVAP) Lab)
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0003-2965-2953
2015 (English)In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 31, no 3, 546-561 p.Article in journal (Refereed) Published
Abstract [en]

Grasping and manipulating everyday objects in a goal-directed manner is an important ability of a service robot. The robot needs to reason about task requirements and ground these in the sensorimotor information. Grasping and interaction with objects are challenging in real-world scenarios, where sensorimotor uncertainty is prevalent. This paper presents a probabilistic framework for the representation and modeling of robot-grasping tasks. The framework consists of Gaussian mixture models for generic data discretization, and discrete Bayesian networks for encoding the probabilistic relations among various task-relevant variables, including object and action features as well as task constraints. We evaluate the framework using a grasp database generated in a simulated environment including a human and two robot hand models. The generative modeling approach allows the prediction of grasping tasks given uncertain sensory data, as well as object and grasp selection in a task-oriented manner. Furthermore, the graphical model framework provides insights into dependencies between variables and features relevant for object grasping.

Place, publisher, year, edition, pages
2015. Vol. 31, no 3, 546-561 p.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-170982DOI: 10.1109/TRO.2015.2409912ISI: 000356518700003Scopus ID: 2-s2.0-84926395738OAI: oai:DiVA.org:kth-170982DiVA: diva2:841369
Note

QC 20150713

Available from: 2015-07-13 Created: 2015-07-13 Last updated: 2017-12-04Bibliographically approved

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Kragic, Danica

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Song, DanEk, Carl HenrikHübner, KaiKragic, Danica
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