机器人技能的演示持续学习
Continual Learning from Demonstration of Robotics Skills
AI总结：
本文提出基于超网络与神经常微分方程求解器的演示持续学习方法，使机器人能连续学习多个运动技能而不遗忘旧技能，并在LASA及自建真实机器人数据集上验证其优于现有持续学习方法。
摘要：
教授机器人运动技能的方法通常一次只针对单一技能进行训练。能够从演示中学习的机器人，若能具备学习新运动技能而不遗忘过去所学内容的能力，将受益匪浅。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演示持续学习的方法。我们通过实验证明了该方法在记忆长序列轨迹学习任务方面的有效性，且无需存储任何过往演示数据。我们的结果表明，超网络在演示学习方面优于其他最先进的持续学习方法。在实验中，我们使用了流行的LASA基准，以及我们在本文中介绍的两个新数据集——使用真实机器人采集的动觉演示数据集，分别称为HelloWorld和RoboTasks数据集。我们在实体机器人上评估了该方法，并证明了其在学习涉及位置和姿态变化的真实世界机器人任务中的有效性。我们报告了轨迹误差指标和持续学习指标，并提出了两个新的持续学习指标。我们的代码以及新采集的数据集可在 https://github.com/sayantanauddy/clfd 获取。
英文摘要：
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.
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。
