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- W2063533050 abstract "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." @default.
- W2063533050 created "2016-06-24" @default.
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- W2063533050 date "2015-06-01" @default.
- W2063533050 modified "2023-09-30" @default.
- W2063533050 title "Task-Based Robot Grasp Planning Using Probabilistic Inference" @default.
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- W2063533050 doi "https://doi.org/10.1109/tro.2015.2409912" @default.
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