In continual learning from demonstration (CLfD), a robot learns a sequence of real-world motion skills continually from human demonstrations. Recently, hypernetworks have been successful in solving this problem. In this paper, we perform an exploratory study of the effects of different optimizers, initializers, and network architectures on the continual learning performance of hypernetworks for CLfD. Our results show that adaptive learning rate optimizers work well, but initializers specially designed for hypernetworks offer no advantages for CLfD. We also show that hypernetworks that are capable of stable trajectory predictions are robust to different network architectures. Our open-source code is available at https://github.com/sebastianbergner/ExploringCLFD.

翻译：在基于演示的持续学习（CLfD）中，机器人通过人类演示持续学习一系列现实世界运动技能。近年来，超网络在解决该问题上取得了成功。本文通过探索性研究，分析了不同优化器、初始化器及网络架构对超网络在CLfD中持续学习性能的影响。结果表明，自适应学习率优化器表现良好，但专为超网络设计的初始化器对CLfD无显著优势。同时，具有稳定轨迹预测能力的超网络对不同网络架构具有鲁棒性。我们的开源代码见 https://github.com/sebastianbergner/ExploringCLFD。