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

Nandhakumar， N.弗吉尼亚大学$49,996 - 12 most SGER：非刚性物体的发现驱动操作：表示、感知和规划该奖项在SGER模式下，资助通过感觉输入发现运动协调技能的机器的初步探索。最近的心理物理学研究表明，人类可以通过对以前没有经验的任务的感觉输入来发现适当的运动协调技能。pi目前正在开发一种基于空间雅可比矩阵的框架，用于设计机器人，非刚性对象操作策略，以模仿人类的这种能力。与现有算法不同，使用该框架导出的策略不需要使用先验变形模型，因为在此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依赖的，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，以实现准确的重新配置。对于一般的柔性对象，空间雅可比集合会非常大，并且不能有效地学习。因此，在这项初步研究中，我们将确定在可接受的位置误差范围内将物体从初始状态移动到最终状态的过程中必须获得哪些空间雅可比矩阵子集。实验研究还将确定创建一组规范形状以减小构型空间大小的可能性。

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.