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Planning Multi-Fingered Grasps as Probabilistic Inference in a Learned Deep Network
[article]
2018
arXiv
pre-print
We propose a novel approach to multi-fingered grasp planning leveraging learned deep neural network models. We train a convolutional neural network to predict grasp success as a function of both visual information of an object and grasp configuration. We can then formulate grasp planning as inferring the grasp configuration which maximizes the probability of grasp success. We efficiently perform this inference using a gradient-ascent optimization inside the neural network using the
arXiv:1804.03289v1
fatcat:trqps4vy25bj7j23pv4z7zan2i