CAREER: Quantifying Humanlike Enveloping Grasps

职业：量化类人包围抓握

• 批准号: 0343161
• 负责人: Nancy Pollard
• 金额: $ 19.4万
• 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: 2003
• 资助国家: 美国
• 起止时间: 2003-07-01 至 2007-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0343161&HistoricalAwards=false
• 关键词: CAREER; Quantifying; Humanlike; Enveloping; Grasps

Over the past decade, our ability to produce graphicalimages has improved to the extent that we can createimaginary scenes that are virtually indistinguishable fromreality. Digital humans have been called the last frontierin this march to graphical realism, and the area of humananimation has also seen dramatic developments. Increasinguse of motion capture data and new techniques formanipulating that data allow us to reproduce human motion atan extremely high level of fidelity. Graphically generatedcharacters in video games and films can seem uncannily real.Pending the development of easy-to-use tools for directingdigital humans, we should soon see digital humans asplausible user interfaces, and animated characters willbecome much more prevalent in education, demonstration, andtraining applications. If digital humans are the lastfrontier in realistic computer graphics, the last frontierin realistic digital humans is generating believable handmotion. Human hands are beautiful and complex mechanisms,amazing in their utility and adaptability. It is argued thatit is our hands that make us human, and that hand evolutionwas a primary factor in the development of intelligence.Hand use in autonomous digital human characters, however, isgenerally quite unconvincing. Hands may be placed in asingle frozen pose, and interaction between characters andobjects is avoided when possible. The main problem is thatgeometric models of the human hand have far too muchflexibility. This flexibility makes working with handsdifficult even for trained animators, and it poses atremendous challenge for creating autonomous characters thatmust interact with their environment. I believe that the keyto making further progress in hand motion for digitalcharacters is much more detailed consideration of theanatomy of the human hand. In analysis of human grasps, forexample, critically important considerations include theamount of contact between finger pads, palm, and object; theability of muscles to produce or resist task force; and thestabilization roles of fingers and muscles, yet none ofthese issues have been explored in grasp synthesis researchin either the robotics or computer graphics communities. Inpursuit of the goal of believable hand use for digitalcharacters, we propose an anatomy-based model of humangrasping. In particular, we propose a tendon-based qualitymeasure for humanlike enveloping grasps, and we plan toevaluate this quality measure (1) for ability todiscriminate between grasps, (2) as a predictor of graspforces, and (3) for use in modeling grasp acquisition.Because of the strong emphasis on human anatomy, thisresearch has the potential for additional impact outsidegraphics and animation in areas including ergonomics (tooldesign), robotics (robot hand design), and anthropology(research in human hand evolution and tool use). Theeducational portion of this proposal focuses on teaching andmentoring of undergraduates. The research ideas, techniques,and results will be incorporated into a course at Brown thatattracts both un-dergraduate and graduate students, and willprovide them with an opportunity to learn and experiment ina problem domain that is a nice mix of computationalgeometry and numerical optimization, grounded in humananatomy and supported by data. A special effort will be madeto include undergraduate women in the research program, forexample, through the CRA Distributed Mentor Program.Research results will include a library of example graspsand applied forces, as well as a tool for adapting theexamples to new hand and object geometries. Once thisresearch is published, the data and tools will be madeavailable to other researchers on the web, and should serveas a useful resource for creating digital characters foreducation, entertainment, and training applications.

在过去的十年里，我们制作图形图像的能力已经提高到了这样一种程度，即我们可以模拟那些实际上与现实无法区分的想象场景。数字人类被称为图形现实主义的最后一个前沿，而人类动画领域也有了戏剧性的发展。越来越多的运动捕捉数据的使用和操纵这些数据的新技术使我们能够以极高的保真度再现人体运动。电子游戏和电影中的图形生成角色看起来出奇的真实的。在开发出易于使用的指导数字人的工具之前，我们很快就会将数字人视为合理的用户界面，而动画角色将在教育中变得更加普遍，演示和培训应用。如果说数字人是现实计算机图形学的最后一个前沿，那么现实数字人的最后一个前沿就是生成可信的手部动作。人类的手是美丽而复杂的机械装置，其实用性和适应性令人惊叹。有人认为是手使我们成为人类，而手的进化是智力发展的主要因素。然而，在自主的数字人类角色中使用手通常是不令人信服的。手可能会被放置在一个单一的冻结姿势，并在可能的情况下避免角色和角色之间的互动。主要的问题是人手的几何模型有太多的灵活性。这种灵活性使得即使是受过训练的动画师也很难用手工作，这对创造必须与环境互动的自主角色构成了巨大的挑战。我相信，在数字角色的手部运动方面取得进一步进展的关键是更详细地考虑人手的解剖结构。例如，在分析人类抓握时，至关重要的考虑因素包括指垫、手掌和物体之间的接触量;肌肉产生或抵抗任务力的能力;以及手指和肌肉的稳定作用，但这些问题都没有在机器人或计算机图形学领域的抓握综合研究中进行过探索。为了实现对数字字符的可信手部使用，我们提出了一个基于解剖学的人类抓取模型。特别是，我们提出了一种基于肌腱的质量测量方法，用于测量类似人类的包络抓握，我们计划评估这种质量测量方法（1）区分抓握的能力，（2）作为抓握力的预测器，（3）用于建模抓握获取。由于对人体解剖学的强调，这项研究有可能在图形和动画之外的包括人体工程学在内的领域产生额外的影响（工具设计），机器人学（机器人手设计）和人类学（研究人手的进化和工具的使用）。本提案的教育部分侧重于本科生的教学和指导。研究思路，技术和结果将被纳入布朗大学的一门课程，吸引了本科生和研究生，并将为他们提供一个学习和实验的机会，这是一个很好的混合计算几何和数值优化的问题域，以人体解剖学为基础，并得到数据的支持。一个特别的努力将包括女大学生在研究计划，例如，通过CRA分布式导师计划。研究成果将包括一个图书馆的例子抓和施加的力量，以及一个工具，使例子适应新的手和物体的几何形状。一旦这项研究发表，数据和工具将在网络上提供给其他研究人员，并应作为一个有用的资源，为教育，娱乐和培训应用程序创建数字字符。