Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task
Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage Task
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发表时间：
2017-07
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通讯作者：
Stephen James;A. Davison;Edward Johns
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作者：
Stephen James;A. Davison;Edward Johns
端到端的机器人操作和抓取控制正在成为一个有吸引力的替代传统的流水线方法。然而，端到端方法往往要么训练缓慢，要么表现出很少或根本没有通用性，要么缺乏完成长期或多阶段任务的能力。在本文中，我们展示了两种简单的技术可以导致多阶段任务的端到端（图像到速度）执行，这类似于一个简单的整理例程，而没有看到一个真实的图像。这涉及到定位，达到，并抓住一个立方体，然后找到一个篮子，并下降立方体内。为了实现这一点，机器人轨迹在模拟器中计算，以收集一系列完成任务的控制速度。然后，训练CNN将观察到的图像映射到速度，使用域随机化来实现对真实的世界图像的概括。结果表明，我们能够成功地完成任务，在真实的世界的能力，推广到新的环境，包括那些动态照明条件，干扰对象，和移动的对象，包括篮子本身。我们相信我们的方法是简单的，高度可扩展的，并且能够学习长期任务，这些任务到目前为止还没有在端到端机器人控制中显示出最先进的水平。
End-to-end control for robot manipulation and grasping is emerging as an attractive alternative to traditional pipelined approaches. However, end-to-end methods tend to either be slow to train, exhibit little or no generalisability, or lack the ability to accomplish long-horizon or multi-stage tasks. In this paper, we show how two simple techniques can lead to end-to-end (image to velocity) execution of a multi-stage task, which is analogous to a simple tidying routine, without having seen a single real image. This involves locating, reaching for, and grasping a cube, then locating a basket and dropping the cube inside. To achieve this, robot trajectories are computed in a simulator, to collect a series of control velocities which accomplish the task. Then, a CNN is trained to map observed images to velocities, using domain randomisation to enable generalisation to real world images. Results show that we are able to successfully accomplish the task in the real world with the ability to generalise to novel environments, including those with dynamic lighting conditions, distractor objects, and moving objects, including the basket itself. We believe our approach to be simple, highly scalable, and capable of learning long-horizon tasks that have until now not been shown with the state-of-the-art in end-to-end robot control.