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.

中文翻译：

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学习转移欠驱动软机器人手的动态模型

迁移学习是一种通过利用来自另一个域的数据来绕过一个域中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使用物理机器人收集数据，这可能既耗时又会导致磨损。使用神经网络执行此操作的最常见方法是采用现有的神经网络，并简单地使用新数据对其进行更多训练。然而，我们表明，在某些情况下，这可能导致性能比简单地使用没有自适应的转移模型更差。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出现混乱或发散的行为。我们推导出训练转换模型的 Lyapunov 指数的上限，并展示两种利用这种洞察力的方法。两者都显示出比传统微调的显着改进。在真正的欠驱动软机械手上进行的实验清楚地证明了将动态模型从一只手转移到另一只手的能力。