Integrating robots in complex everyday environments requires a multitude of problems to be solved. One crucial feature among those is to equip robots with a mechanism for teaching them a new task in an easy and natural way. When teaching tasks that involve sequences of different skills, with varying order and number of these skills, it is desirable to only demonstrate full task executions instead of all individual skills. For this purpose, we propose a novel approach that simultaneously learns to segment trajectories into reoccurring patterns and the skills to reconstruct these patterns from unlabelled demonstrations without further supervision. Moreover, the approach learns a skill conditioning that can be used to understand possible sequences of skills, a practical mechanism to be used in, for example, human-robot-interactions for a more intelligent and adaptive robot behaviour. The Bayesian and variational inference based approach is evaluated on synthetic and real human demonstrations with varying complexities and dimensionality, showing the successful learning of segmentations and skill libraries from unlabelled data.
翻译:将机器人融入复杂的日常环境中需要解决许多问题。 其中的一个关键特征是给机器人配备一个以简单和自然的方式教授新任务的机制。当教学任务涉及不同技能的序列时,这些技能的顺序和数量各不相同,因此最好只展示完全的任务处决,而不是所有个人技能。为此,我们建议一种新颖的方法,既学习将分解成重复的轨迹,又学习在没有进一步监督的情况下从无标签的演示中重建这些模式的技能。此外,该方法还学习了一种技能调节,可用来理解可能的技能序列,这是一种实用的机制,用于例如人-机器人相互作用,以便更聪明和适应性更强的机器人行为。基于巴耶斯和变异推论的方法是对复杂和多面性的合成和真实人类演示进行评估,显示从无标签的数据中成功学习了分解和技能图书馆。