Learning to Transfer Dynamic Models of Underactuated Soft Robotic Hands
Learning to Transfer Dynamic Models of Underactuated Soft Robotic Hands
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DOI：
10.1109/icra40945.2020.9197300
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
Liam Schramm;A. Sintov;Abdeslam Boularias
中科院分区：
文献类型：
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作者：
Liam Schramm;A. Sintov;Abdeslam Boularias
迁移学习是一种通过利用来自另一个领域的数据来绕过一个领域中的数据限制的流行方法。这在机器人技术中特别有用，因为它允许从业者减少物理机器人的数据收集，这可能是耗时的，并会导致磨损。使用神经网络最常见的方法是使用现有的神经网络，并使用新数据对其进行更多的训练。然而，我们表明，在某些情况下，这可能会导致显着更差的性能比简单地使用转移模型没有适应。我们发现，这些问题的一个主要原因是，在少量数据上训练的模型在某些区域可能会有混乱或发散的行为。我们推导出一个训练的过渡模型的李雅普诺夫指数的上限，并展示了两种方法，利用这一见解。这两种方法都比传统的微调方法有显著的改进。在真实的欠驱动软机器人手上进行的实验清楚地表明了将动态模型从一只手转移到另一只手的能力。
Transfer learning is a popular approach to bypassing data limitations in one domain by leveraging data from another domain. This is especially useful in robotics, as it allows practitioners to reduce data collection with physical robots, which can be time-consuming and cause wear and tear. The most common way of doing this with neural networks is to take an existing neural network, and simply train it more with new data. However, we show that in some situations this can lead to significantly worse performance than simply using the transferred model without adaptation. We find that a major cause of these problems is that models trained on small amounts of data can have chaotic or divergent behavior in some regions. We derive an upper bound on the Lyapunov exponent of a trained transition model, and demonstrate two approaches that make use of this insight. Both show significant improvement over traditional fine-tuning. Experiments performed on real underactuated soft robotic hands clearly demonstrate the capability to transfer a dynamic model from one hand to another.