论文标题
从少数示威中学习任务参数化技能
Learning Task-Parameterized Skills from Few Demonstrations
论文作者
论文摘要
如今，机器人脱离了重复的任务，需要适应不同情况的多功能技能。任务参数化的学习通过在任务参数中编码相关的上下文信息来改善运动策略的概括，从而实现灵活的任务执行。但是，培训这样的政策通常需要在不同情况下收集多次示威。为了全面创造不同的情况，这是非平凡的，因此使该方法不适用于现实世界中的问题。因此，需要较少的示威/情况培训。本文提出了一个新颖的概念，可以通过合成数据来增强原始培训数据集以改进政策，从而可以学习任务参数化的技能，而示范很少。
Moving away from repetitive tasks, robots nowadays demand versatile skills that adapt to different situations. Task-parameterized learning improves the generalization of motion policies by encoding relevant contextual information in the task parameters, hence enabling flexible task executions. However, training such a policy often requires collecting multiple demonstrations in different situations. To comprehensively create different situations is non-trivial thus renders the method less applicable to real-world problems. Therefore, training with fewer demonstrations/situations is desirable. This paper presents a novel concept to augment the original training dataset with synthetic data for policy improvements, thus allows learning task-parameterized skills with few demonstrations.
