We introduce a Learning from Demonstration (LfD) approach for contact-rich manipulation tasks with articulated mechanisms. The extracted policy from a single human demonstration generalizes to different mechanisms of the same type and is robust against environmental variations. The key to achieving such generalization and robustness from a single human demonstration is to autonomously augment the initial demonstration to gather additional information through purposefully interacting with the environment. Our real-world experiments on complex mechanisms with multi-DOF demonstrate that our approach can reliably accomplish the task in a changing environment. Videos are available at the: https://sites.google.com/view/rbosalfdec/home

翻译：我们采用“从示范中学习”的方法,用明确的机制进行丰富的接触操作任务。从单一人类演示中提取的政策向同一类型的不同机制概括,并且能够抵御环境的变异。从单一人类演示中实现这种普遍性和稳健性的关键在于自主地增加初步示范,以便通过有目的地与环境互动来收集更多的信息。我们关于多功能多功能多功能多功能的复杂机制的现实世界实验表明,我们的方法可以在不断变化的环境中可靠地完成这项任务。视频可见于:https://sites.google.com/view/rbosalfdec/home。