超网络的优化器、初始化器和架构对基于演示的持续学习的影响
Effect of Optimizer, Initializer, and Architecture of Hypernetworks on Continual Learning from Demonstration
AI总结：
本文探索了超网络的优化器、初始化器和网络架构对基于演示的持续学习（CLfD）性能的影响，发现自适应学习率优化器效果良好，专用初始化器无优势，且稳定预测轨迹的超网络对架构具有鲁棒性。
摘要：
在基于演示的持续学习（CLfD）中，机器人持续从人类演示中学习一系列真实世界的运动技能。最近，超网络在解决该问题上取得了成功。本文对不同的优化器、初始化器和网络架构如何影响超网络在CLfD中的持续学习性能进行了探索性研究。结果表明，自适应学习率优化器效果良好，但专为超网络设计的初始化器对CLfD并无优势。我们还证明了能够进行稳定轨迹预测的超网络对不同网络架构具有鲁棒性。开源代码可在https://github.com/sebastianbergner/ExploringCLFD获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
