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arXiv · 2007.04499

Robotic Grasping using Deep Reinforcement Learning

Shirin Joshi·Sulabh Kumra·Ferat Sahin
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Abstract

In this work, we present a deep reinforcement learning based method to solve the problem of robotic grasping using visio-motor feedback. The use of a deep learning based approach reduces the complexity caused by the use of hand-designed features. Our method uses an off-policy reinforcement learning framework to learn the grasping policy. We use the double deep Q-learning framework along with a novel Grasp-Q-Network to output grasp probabilities used to learn grasps that maximize the pick success. We propose a visual servoing mechanism that uses a multi-view camera setup that observes the scene which contains the objects of interest. We performed experiments using a Baxter Gazebo simulated environment as well as on the actual robot. The results show that our proposed method outperforms the baseline Q-learning framework and increases grasping accuracy by adapting a multi-view model in comparison to a single-view model.

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Shirin Joshi, Sulabh Kumra, Ferat Sahin. 2020-07-09. Robotic Grasping using Deep Reinforcement Learning. https://arxiv.org/abs/2007.04499

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