CGV: EAGER: Simulation-Based Manipulation Capture for Dexterous Character Animation

CGV：EAGER：基于模拟的灵巧角色动画操作捕捉

• 批准号: 1145640
• 负责人: Nancy Pollard
• 金额: $ 8.1万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1145640&HistoricalAwards=false
• 关键词: CGV; EAGER; Simulation; Based; Manipulation

Through our hands, we communicate, care for ourselves and others, and use tools to affect our world. However, our understanding of how we control our hands to accomplish such feats is still in its infancy. Dexterous manipulation is especially challenging to study, as even "simple" manipulation tasks are complex in their details. The task of lifting a wrench into the hand, for example, can be decomposed into five separate actions, each of which may require a specialized control strategy. It is important to study human examples of such activities; through understanding human expertise we can reach practical outcomes such as physically intelligent animated characters for training and remote communication, robots capable of unprecedented dexterity, and prosthetic designs that far exceed the current state of the art in their elegance and functionality.The key hurdle in making a significant advance in these areas is the difficulty of capturing human manipulation in a form that facilitates analysis and study. The rapid sequence of contact events, the hand's large number of degrees of freedom, and close contact between the hand and object all contribute to creating an impossible capture task using traditional methods. The investigators are developing an alternative: simulation motion capture, where a user interacts with and guides a running simulation. Through this innovative approach, details such as contact timing, contact area, and contact forces are made available for the first time for general manipulation tasks. Key innovations include a fast simulation system for a deformable human hand (or full body) and novel techniques to control such a high degree of freedom simulation with intent and precision. In parallel, the investigators create a database of manipulation tasks, study new languages for action segmentation and control law development, develop robust autonomous controllers for grasping and manipulation, and study novel classifiers for recognition of affordances.

通过我们的双手，我们沟通，关心自己和他人，并使用工具来影响我们的世界。然而，我们对如何控制我们的手来完成这样的壮举的理解仍然处于起步阶段。 灵巧的操作是特别具有挑战性的研究，因为即使是“简单的”操作任务在他们的细节是复杂的。 举个例子，将扳手举到手中的任务可以分解为五个独立的动作，每个动作都需要专门的控制策略。 必须研究这类活动的人类实例;通过了解人类的专业知识，我们可以达到实际的成果，如物理智能动画人物的培训和远程通信，机器人能够前所未有的灵活性，在这些领域取得重大进展的关键障碍是难以捕捉人类的视觉特征，以便于分析和研究的形式进行操作。 接触事件的快速序列、手的大量自由度以及手与物体之间的紧密接触都有助于使用传统方法创建不可能的捕获任务。 研究人员正在开发一种替代方案：模拟动作捕捉，其中用户与运行的模拟进行交互并指导。通过这种创新方法，首次为一般操作任务提供了接触时间、接触面积和接触力等细节。 关键创新包括用于可变形人手（或全身）的快速模拟系统，以及用于控制这种具有意图和精度的高自由度模拟的新技术。 与此同时，研究人员创建了一个操作任务数据库，研究新的语言动作分割和控制律的发展，开发强大的自主控制器的把握和操纵，并研究新的分类识别的启示。