Utilizing a robot in a new application requires the robot to be programmed at each time. To reduce such programmings efforts, we have been developing ``Learning-from-observation (LfO)'' that automatically generates robot programs by observing human demonstrations. One of the main issues with introducing this LfO system into the domain of household tasks is the cluttered environments, which cause difficulty in determining which elements are important for task execution when observing demonstrations. To overcome this issue, it is necessary for the system to have common sense shared with the human demonstrator. This paper addresses three relationships that LfO in the household domain should focus on when observing demonstrations and proposes representations to describe the common sense used by the demonstrator for optimal execution of task sequences. Specifically, the paper proposes to use labanotation to describe the postures between the environment and the robot, contact-webs to describe the grasping methods between the robot and the tool, and physical and semantic constraints to describe the motions between the tool and the environment. Then, based on these representations, the paper formulates task models, machine-independent robot programs, that indicate what to do and how to do. Third, the paper explains the task encoder to obtain task models and task decoder to execute the task models on the robot hardware. Finally, this paper presents how the system actually works through several example scenes.

翻译：在将机器人应用于新场景时，每次都需要对其进行编程。为减少这种编程负担，我们一直在开发“学习-从-观察”（LfO）技术，该技术通过观察人类演示自动生成机器人程序。将LfO系统引入家庭任务领域的主要问题之一是环境杂乱，这导致在观察演示时难以确定哪些元素对任务执行至关重要。为克服这一难题，系统需要具备与人类演示者共享的常识。本文探讨了家庭领域LfO在观察演示时应关注的三种关系，并提出相应表述来描述演示者为优化任务序列执行所运用的常识。具体而言，论文提出使用拉班舞谱描述环境与机器人之间的姿态关系，使用接触网描述机器人工具间的抓取方式，以及使用物理和语义约束描述工具与环境间的运动关系。基于这些表述，论文进一步构建了任务模型——即独立于机器人的程序，用以指示“做什么”和“如何做”。随后，本文阐述了获取任务模型的任务编码器，以及将任务模型在机器人硬件上执行的任务解码器。最后，通过多个实例场景展示了该系统的实际运行过程。