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Towards a Humanoid-Oriented Movement Writing

机译:朝着人形导向的运动写作

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This paper introduces humanoid-oriented movement writing (HOM), focusing on a notation in which body postures allow easy visual interpretation by both humans and humanoid robots. HOM Writing, derived from Sutton Movement Writing and Shorthand [1], is a natural modality for encoding the movements that humans perform during various work activities. Beyond its use as a record of human movement, the intent is to use the writing as a modality of communicating to robots what movements to execute. Humanoid robots could directly map the key postures represented in the notation to their own postures, imitating the postures captured in the description, and calculating intermediate postures by interpolation. A motion generator would ensure the motor control needed to create continuous movements. This paper focuses on the generation of the activity movement scripts, and addresses two modalities of producing the scripts: 1) 'hand-coded' by a human, using an editor, and 2) automatic extraction from video, e.g. from video-recordings of a human performing an activity. A software tool developed to allow easy script writing, the HOM Editor, is described and illustrated in hand-coding of a sequence of movements. The automatic generation of scripts from video is done using the pose estimation system by Yang and Ramanan [2], which takes an image and produces the joint coordinates of the limb parts; this is illustrated with a task of moving and arranging chairs. The posture extraction from a video provided by a single camera may often lead to occlusions of body parts during activities in which objects are manipulated. We show the advantages of using an additional camera, which significantly increases the correct posture estimation, and discuss how to further improve the automatic generation of scripts.
机译:本文介绍了人形导向的运动写作(HOM),专注于身体姿势的符号,这些姿势允许人类和人形机器人容易地解释。来自Sutton运动写作和速记[1]的HOM写作是一种用于编码人类在各种工作活动期间执行的运动的自然模式。除了作为人类运动的记录之外,意图是使用写作作为与机器人通信的模当的执行。人形机器人可以直接映射到它们自己的姿势中表示的关键姿势,模仿在描述中捕获的姿势,并通过插值计算中间姿势。运动发生器将确保采用连续运动所需的电机控制。本文重点介绍了活动运动脚本的生成,并通过人类,使用编辑器和2)从视频中自动提取,解决人类的脚本的两种方式:1)“手工编码”,例如,使用编辑器和2)自动提取。从人类进行活动的视频录制。开发用于允许简单脚本写入HOM编辑器的软件工具,并在手中编码移动序列的手动编码。使用阳和ramanan [2]的姿势估计系统自动生成来自视频的脚本[2],它拍摄图像并产生肢体部件的关节坐标;这是通过移动和安排椅子的任务说明的。从单个摄像机提供的视频的姿势提取可能在操纵物体的活动期间,通常导致身体部位的闭塞。我们展示了使用额外的相机的优点,这显着增加了正确的姿势估计,并讨论了如何进一步改善自动生成脚本。

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