  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 movement skills without forgetting
what was learned in the past. 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
this 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 continual learning
approaches for learning from demonstration. In our experiments, we use the
popular LASA benchmark, and two new datasets of kinesthetic demonstrations
collected with a real robot that we introduce in this paper called the
HelloWorld and RoboTasks datasets. We evaluate our approach on a physical robot
and demonstrate its effectiveness in learning real-world robotic tasks
involving changing positions as well as orientations. We report both trajectory
error metrics and continual learning metrics, and we propose two new continual
learning metrics. Our code, along with the newly collected datasets, is
available at https://github.com/sayantanauddy/clfd.
