Adapting control policies from simulation to reality using a pairwise loss
Adapting control policies from simulation to reality using a pairwise loss
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DOI：
10.1007/978-3-030-33950-0_23
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发表时间：
2018-07
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影响因子：
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通讯作者：
Ulrich Viereck;Kate Saenko;Robert W. Platt
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文献类型：
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作者：
Ulrich Viereck;Kate Saenko;Robert W. Platt
本文提出了一种基于成对损失函数的域转移方法，帮助将仿真中学习到的控制策略转移到真实机器人上。我们在“类别级别”操作任务的背景下探索这一想法，其中学习了一种控制策略，使机器人能够执行涉及新对象的交配任务。我们探讨了深度图像作为传感器输入的主要形式的情况。我们的实验结果表明，所提出的方法始终优于仅在模拟中训练的基线方法，或者以简单的方式将真实数据和模拟数据相结合的基线方法。
This paper proposes an approach to domain transfer based on a pairwise loss function that helps transfer control policies learned in simulation onto a real robot. We explore the idea in the context of a “category level” manipulation task where a control policy is learned that enables a robot to perform a mating task involving novel objects. We explore the case where depth images are used as the main form of sensor input. Our experimental results demonstrate that proposed method consistently outperforms baseline methods that train only in simulation or that combine real and simulated data in a naive way.