基于帧加权运动生成的少样本演示学习
Learning from Few Demonstrations with Frame-Weighted Motion Generation
AI总结：
本文提出一种基于参考帧权重的少样本演示学习算法，通过捕捉任务执行中帧的重要性，在仅少量演示的情况下实现高效技能获取，并在真实机器人环境中验证了其有效性。
摘要：
从演示中学习（LfD）使机器人能够通过从人类演示中学习运动策略来获得多样化的技能。它为用户提供了一种直观的接口，可以将新技能传递给机器人，而无需耗时的机器人编程和低效的解决方案探索。在任务执行过程中，机器人运动通常受到环境施加的约束的影响。鉴于此，任务参数化LfD（TP-LfD）将相关上下文信息编码到参考帧中，从而能够更好地将技能泛化到新情况。然而，大多数TP-LfD算法通常需要在各种环境条件下进行多次演示，以确保有足够的统计数据来构建有意义的模型。对于机器人用户来说，创建不同的情境并在所有这些情境下进行演示并非易事。因此，本文提出了一种新颖的算法，从少量演示中学习技能。通过利用参考帧权重来捕捉任务执行过程中帧的重要性或相关性，我们的方法展示了出色的技能获取性能，并在真实机器人环境中得到了验证。
英文摘要：
Learning from Demonstration (LfD) enables robots to acquire versatile skills by learning motion policies from human demonstrations. It endows users with an intuitive interface to transfer new skills to robots without the need for time-consuming robot programming and inefficient solution exploration. During task executions, the robot motion is usually influenced by constraints imposed by environments. In light of this, task-parameterized LfD (TP-LfD) encodes relevant contextual information into reference frames, enabling better skill generalization to new situations. However, most TP-LfD algorithms typically require multiple demonstrations across various environmental conditions to ensure sufficient statistics for a meaningful model. It is not a trivial task for robot users to create different situations and perform demonstrations under all of them. Therefore, this paper presents a novel algorithm to learn skills from few demonstrations. By leveraging the reference frame weights that capture the frame importance or relevance during task executions, our method demonstrates excellent skill acquisition performance, which is validated in real robotic environments.
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。
