SGER: Discovery Driven Manipulation of NonRigid Objects: Representation, Sensing and Planning

SGER：发现驱动的非刚性物体操纵：表示、传感和规划

• 批准号: 9616131
• 负责人: N. Nandhakumar
• 金额: $ 5万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-08-15 至 1997-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9616131&HistoricalAwards=false
• 关键词: SGER; Discovery; Driven; Manipulation; NonRigid

IRI-9616131 Nandhakumar, N. University of Virginia $49,996 - 12 mos SGER: Discovery Driven Manipulation of NonRigid Objects: Representation, Sensing and Planning This award, in the SGER mode, funds an initial exploration of machine discovery of motor coordination skills through sensory input. Recent psychophysical studies have revealed that humans can discover proper motor coordination skills through sensory input for tasks in which they have had no previous experience. The PIs are currently developing a spatial Jacobian-based framework for the design of robotic, nonrigid object manipulation strategies which emulate this human ability. Unlike existing algorithms, the strategies derived using this framework do not require the use of a priori deformation models since the necessary object parameters are learned during the process. Since the spatial Jacobians of an object are spatially dependent,the critical obstacle in the framework's continued development is determining the amount of information which must be recovered in the learning or discovery phase of the algorithms for accurate reconfiguration. For general flexible objects, the spatial Jacobian sets will be quite large and not efficiently learnable. Thus, in this preliminary research we will determine what subsets of spatial Jacobians must be obtained by the process to move the object from an initial state to a final state within an acceptable positional error. An experimental investigation will also determine the possibility of creating a set of canonical shapes to reduce the configuration space size as well.

IRI-9616131 Nandhakumar，N. 弗吉尼亚大学$49，996 - 12个月 SGER：发现驱动的非刚性物体操纵：表示，传感和规划 该奖项在SGER模式下资助了通过感官输入对机器发现运动协调技能的初步探索。 最近的心理物理学研究表明，人类可以通过感官输入来发现适当的运动协调技能，这些技能是他们以前没有经验的。 PI目前正在开发一个基于空间雅可比的框架，用于设计模仿人类能力的机器人非刚性物体操作策略。 与现有的算法不同，使用该框架导出的策略不需要使用先验变形模型，因为在该过程中学习了必要的对象参数。 由于对象的空间雅可比矩阵是空间相关的，因此框架继续发展的关键障碍是确定在算法的学习或发现阶段必须恢复的信息量，以进行准确的重新配置。 对于一般的柔性物体，空间雅可比集将是相当大的，并且不能有效地学习。 因此，在这个初步的研究中，我们将确定什么样的空间雅可比矩阵的子集必须通过该过程获得移动的对象从一个初始状态到一个最终状态在一个可接受的位置误差。 实验研究也将确定创建一组规范形状以减小配置空间大小的可能性。