Learning real manipulation tasks from virtual demonstrations using LSTM
Learning real manipulation tasks from virtual demonstrations using LSTM
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发表时间：
2016-03
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通讯作者：
Rouhollah Rahmatizadeh;P. Abolghasemi;A. Behal;Ladislau Bölöni
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作者：
Rouhollah Rahmatizadeh;P. Abolghasemi;A. Behal;Ladislau Bölöni
-帮助残疾人或老年人进行日常生活活动的机器人必须执行复杂的操纵任务。这些任务取决于用户的环境和首选项。因此，从演示中学习(LFD)是一个有希望的选择，它将允许非专家用户向机器人传授不同的任务。不幸的是，从原始演示中学习一般解决方案需要大量数据。对于残疾用户来说，执行这种数量的物理演示是不可行的。在本文中，我们提出了一种用户在虚拟环境中演示操作任务的方法。收集到的演示被用来训练LSTM递归神经网络，该网络可以作为机器人的控制器。我们证明，从虚拟演示中学习的控制器可以用于在物理机器人上成功地执行操作任务。
— Robots assisting disabled or elderly people in activities of daily living must perform complex manipulation tasks. These tasks are dependent on the user's environment and preferences. Thus, learning from demonstration (LfD) is a promising choice that would allow the non-expert user to teach the robot different tasks. Unfortunately, learning general solutions from raw demonstrations requires a significant amount of data. Performing this number of physical demonstrations is unfeasible for a disabled user. In this paper we propose an approach where the user demonstrates the manipulation task in a virtual environment. The collected demonstrations are used to train an LSTM recurrent neural network that can act as the controller for the robot. We show that the controller learned from virtual demonstrations can be used to successfully perform the manipulation tasks on a physical robot.