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Accomplishing Robot Grasping Task Rapidly via Adversarial Training

机译：通过对抗性培训快速完成机器人掌握任务

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摘要

This paper proposes a robotic imitation learning method which integrates the deterministic off-policy reinforcement learning and generative adversarial network. This method allows the robot to implement the grasping task rapidly by learning the reward function from the demonstration data. Firstly, the discriminator is used to learn the reward function from demonstrations, which can guide the generator to complete the robot grasping task. Secondly, the deep deterministic policy gradient method is used as the generator for learning action policy on the basis of discriminator. In particular, the demonstration data is also input into the generator to ensure its performance. Finally, three experiments on the Push and Pick-and-Place tasks are conducted in the GYM robotic environment. Results show that the learning speed of our method is much faster than the stochastic GAIL method, and it can effectively train from the demonstration data in different states of the task. The proposed method can complete the robot grasping task without environmental reward quickly and improve the stability of the training process.
机译：本文提出了一种机器人模仿学习方法，其集成了确定性脱策强化学习和生成的对抗性网络。该方法允许机器人通过从演示数据学习奖励功能来快速实现抓握任务。首先，鉴别者用于从演示中学习奖励功能，这可以指导发电机完成机器人掌握任务。其次，深度确定性政策梯度方法用作基于鉴别者学习行动政策的发电机。特别是，演示数据也输入到发电机中以确保其性能。最后，在健身机器人环境中进行了推动和拾取任务的三个实验。结果表明，我们的方法的学习速度比随机Gail方法快得多，并且可以有效地从任务的不同状态下从演示数据训练。该方法可以快速地完成没有环境奖励的机器人掌握任务，提高培训过程的稳定性。

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