Learning to Transfer Dynamic Models of Underactuated Soft Robotic Hands

Autor:	Schramm, Liam, Sintov, Avishai, Boularias, Abdeslam
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Electrical Engineering and Systems Science - Systems and Control Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: ICRA 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2005.10418 Zobrazit plný text záznamu View this record from Arxiv