Methods for teaching motion skills to robots focus on training for a single skill at a time. Robots capable of learning from demonstration can considerably benefit from the added ability to learn new movements without forgetting past knowledge. To this end, we propose an approach for continual learning from demonstration using hypernetworks and neural ordinary differential equation solvers. We empirically demonstrate the effectiveness of our approach in remembering long sequences of trajectory learning tasks without the need to store any data from past demonstrations. Our results show that hypernetworks outperform other state-of-the-art regularization-based continual learning approaches for learning from demonstration. In our experiments, we use the popular LASA trajectory benchmark, and a new dataset of kinesthetic demonstrations that we introduce in this paper called the HelloWorld dataset. We evaluate our approach using both trajectory error metrics and continual learning metrics, and we propose two new continual learning metrics. Our code, along with the newly collected dataset, is available at https://github.com/sayantanauddy/clfd.
翻译:向机器人传授运动技能的方法侧重于一次的单一技能培训。 能够从演示中学习的机器人可以大大受益于在不忘记过去知识的情况下学习新运动的附加能力。 为此,我们提出一种方法,用超网络和神经普通差异方程解析器从演示中不断学习。 我们从经验上证明我们的方法在记住长的轨迹学习任务序列而无需储存以往演示的任何数据方面的有效性。 我们的结果显示,超网络优于其他最先进的基于正规化的不断学习方法,从演示中学习。 在我们的实验中,我们使用流行的LASA轨迹基准,以及我们在这个名为HelloWorld数据集的论文中介绍的关于动脉学示范的新数据集。我们用轨迹误差指标和持续学习指标来评估我们的方法,我们提出了两个新的持续学习指标。我们的代码和新收集的数据集可以在https://github.com/sayantanauddy/clfd查阅。