Multi-Fingered Grasp Planning via Inference in Deep Neural Networks
@article{Lu2020MultiFingeredGP, title={Multi-Fingered Grasp Planning via Inference in Deep Neural Networks}, author={Qingkai Lu and Mark Van der Merwe and Balakumar Sundaralingam and Tucker Hermans}, journal={ArXiv}, year={2020}, volume={abs/2001.09242} }
We propose a novel approach to multi-fingered grasp planning leveraging learned deep neural network models. We train a voxel-based 3D convolutional neural network to predict grasp success probability 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. In addition, we learn a prior over grasp configurations as a mixture density network conditioned… CONTINUE READING
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