Oct 17, 2017 · We explore a novel data generation pipeline for training a deep neural network to perform grasp planning that applies the idea of domain randomization to ...
In this work, we explore a novel data generation pipeline for training a deep neural network to perform grasp planning that applies the idea of domain ...
A novel data generation pipeline for training a deep neural network to perform grasp planning that applies the idea of domain randomization to object ...
Deep learning-based robotic grasping has made significant progress thanks to algorithmic improvements and increased data availability.
Domain Randomization and Generative Models for Robotic ...
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Additionally, Tobin et al. [192] suggested applying domain randomization to object synthesis when training DRL models to perform grasp planning, which yielded ...
In this work, we explore a novel data generation pipeline for the need for accurate 3D models of the objects in question, training a deep neural network to ...
Domain randomization for transferring deep neural networks from simulation to the real world. In2017 IEEE/RSJ international conference on intelligent robots and ...
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Jun 23, 2019 · "Domain randomization and generative models for robotic grasping." 2018 IEEE/RSJ. International Conference on Intelligent Robots and Systems ...
In this work, we explore a novel data generation pipeline for training a deep neural network to perform grasp planning that applies the idea of domain ...
[192] Domain Randomization and Generative Models for Robotic Grasping, ... [157] Domain Randomization for Transferring Deep Neural Networks from Simulation ...