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.

翻译：从示范中教授机器人运动技能的方法通常集中在一次训练单一技能上。具备从示范中学习能力的机器人，若能进一步获得学习新运动技能而不遗忘已学内容的能力，将显著受益。为此，我们提出一种利用超网络和神经常微分方程求解器进行持续示范学习的方法。实验证明，该方法能有效记忆长序列的轨迹学习任务，而无需存储任何过去的示范数据。结果表明，超网络在从示范中学习方面优于其他最先进的持续学习方法。实验中，我们使用流行的LASA基准测试以及两个由真实机器人采集的运动学示范新数据集（本文引入的HelloWorld和RoboTasks数据集）。我们在实体机器人上评估了该方法，并展示了其在涉及位置和方向变化的真实机器人任务学习中的有效性。我们报告了轨迹误差指标和持续学习指标，并提出了两项新的持续学习指标。我们的代码及新收集的数据集可在 https://github.com/sayantanauddy/clfd 获取。