CGV: Small: Simulation Motion Capture of Dexterous Manipulation

CGV：小：灵巧操作的模拟动作捕捉

• 批准号: 1218182
• 负责人: Nancy Pollard
• 金额: $ 49.98万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218182&HistoricalAwards=false
• 关键词: CGV; Small; Simulation; Motion; Capture

This research involves novel techniques for capturing natural human manipulation actions in all of their complexity. Consider how we prepare food, pick up and use a common tool such as a wrench, or plant a flower. We would like to learn from human examples of such complex actions, so that we can portray convincing graphical renditions of human characters for applications ranging from education, training, and entertainment to exposition and scientific study. Broader impact includes use of databases and the capture tool developed as part of this research for development of dexterous robotic manipulation, design of prosthetics, and understanding of human grasping and manipulation. The visual nature and appeal of this research makes it especially suited for mentoring of undergraduates and women, as well as for outreach to spark interest in science for K-12 students.Traditional and established approaches to motion capture, such as optical approaches, cannot handle the complex, detailed, high degree of freedom interactions that must be observed to capture dexterous manipulation actions. The alternative pursued in this research is simulation motion capture, where a user interacts with and guides a running simulation. Enabling technologies include (1) real-time simulation of a skeleton driven deformable hand in close interaction with simulated objects, (2) novel control algorithms and interface techniques to allow direct control of such simulations, (3) a new language for segmentation and control law development, based on user actions during the simulation, and (4) novel manipulation objective functions learned from direct observation of human actions. Benefits of simulation capture over current capture technologies include little or no postprocessing, precise representation of contact forces, fast trial and error, ability to make use of a variety of means for control, and ability to use a variety of input devices. The resulting capture system will be inexpensive and broady accessible.

这项研究涉及新的技术，捕捉自然的人类操作动作的所有复杂性。 想想我们如何准备食物，拿起并使用扳手等常用工具，或者种花。 我们想从人类的例子中学习这种复杂的行动，这样我们就可以描绘出令人信服的图形再现的人类字符的应用范围从教育，培训，娱乐展览和科学研究。 更广泛的影响包括使用数据库和捕获工具开发的灵巧的机器人操作，假肢的设计，以及人类的把握和操纵的理解，作为本研究的一部分。 这项研究的视觉性质和吸引力使其特别适合于指导本科生和女性，以及推广到激发K-12学生对科学的兴趣。传统和成熟的运动捕捉方法，如光学方法，无法处理复杂，详细，高自由度的相互作用，必须观察到捕捉灵巧的操作动作。 在这项研究中所追求的替代方案是模拟运动捕捉，其中用户与运行的模拟进行交互并引导。 使能技术包括（1）实时模拟骨骼驱动的可变形手与模拟对象的密切交互，（2）允许直接控制此类模拟的新型控制算法和接口技术，（3）一种新的分割语言和控制律开发，基于模拟期间的用户操作，和（4）从直接观察人类行为中学习的新的操纵目标函数。 模拟捕获优于当前捕获技术的优点包括很少或没有后处理、接触力的精确表示、快速试错、利用各种控制手段的能力以及使用各种输入设备的能力。 由此产生的捕获系统将是廉价的和广泛的访问。