Learning task constraints for robot grasping using graphical models

D. Song*, K. Huebner, V. Kyrki, D. Kragic

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

78 Sitaatiot (Scopus)

Abstrakti

This paper studies the learning of task constraints that allow grasp generation in a goal-directed manner. We show how an object representation and a grasp generated on it can be integrated with the task requirements. The scientific problems tackled are (i) identification and modeling of such task constraints, and (ii) integration between a semantically expressed goal of a task and quantitative constraint functions defined in the continuous object-action domains. We first define constraint functions given a set of object and action attributes, and then model the relationships between object, action, constraint features and the task using Bayesian networks. The probabilistic framework deals with uncertainty, combines apriori knowledge with observed data, and allows inference on target attributes given only partial observations. We present a system designed to structure data generation and constraint learning processes that is applicable to new tasks, embodiments and sensory data. The application of the task constraint model is demonstrated in a goal-directed imitation experiment.

AlkuperäiskieliEnglanti
OtsikkoIEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings
Sivut1579-1585
Sivumäärä7
DOI - pysyväislinkit
TilaJulkaistu - 2010
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE/RSJ International Conference on Intelligent Robots and Systems - Taipei, Taiwan
Kesto: 18 lokak. 201022 lokak. 2010
Konferenssinumero: 23

Conference

ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems
LyhennettäIROS
Maa/AlueTaiwan
KaupunkiTaipei
Ajanjakso18/10/201022/10/2010

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