CAREER: Quantifying Humanlike Enveloping Grasps

职业：量化类人包围抓握

• 批准号: 0093072
• 负责人: Nancy Pollard
• 金额: $ 32.46万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-04-15 至 2004-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0093072&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我们可以创造出与现实几乎无法区分的想象场景的程度。数字人类被称为向图形现实主义进军的最后前沿，人类动画领域也取得了巨大的发展。越来越多地使用动作捕捉数据和操纵这些数据的新技术，使我们能够以极高的保真度重现人类动作。视频游戏和电影中图形生成的角色看起来非常真实。在开发出易于使用的指导数字人的工具之前，我们应该很快就会看到数字人作为合理的用户界面，动画角色将在教育、演示和培训应用中变得更加普遍。如果说数字人是逼真计算机图形学的最后一个前沿，那么逼真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，其实用性和适应性令人惊叹。有人认为，正是我们的手使我们成为人类，而手的进化是智力发展的一个主要因素。然而，自动数字人类角色的手部操作通常不太令人信服。手可能被放置在一个固定的姿势，角色和物体之间的互动尽可能避免。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对训练有素的动画师来说，用手工作也很困难，这对创造必须与环境互动的自主角色构成了巨大的挑战。我认为，在数字人物的手部动作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，在分析人类抓取时，至关重要的考虑因素包括指垫、手掌和物体之间的接触量；肌肉力量：肌肉产生或抵抗特别力量的能力；以及手指和肌肉的稳定作用，然而这些问题都没有在机器人或计算机图形学社区的抓取合成研究中被探索过。为了追求数字字符可信的手部使用目标，我们提出了一种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人类包膜抓取的质量度量，我们计划评估这种质量度量(1)区分抓取的能力，(2)作为抓取力的预测因子，以及(3)用于抓取获取建模。由于对人体解剖学的强调，这项研究有可能对图形学和动画以外的领域产生额外的影响，包括人体工程学（工具设计）、机器人技术（机器人手设计）和人类学（人手进化和工具使用研究）。该提案的教育部分侧重于对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，吸引本科生和研究生，并将为他们提供一个学习和实验问题领域的机会，这是一个计算几何和数值优化的良好组合，以人体解剖学为基础，并有数据支持。将作出特别努力，例如通过CRA分布式导师计划，将本科女性纳入研究计划。研究成果将包括一个例子掌握和应用的力量，以及一个工具，以适应新的手和物体几何的例子。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，并应作为创建数字字符教育，娱乐和培训应用程序的有用资源。