Data Structures for Robotic Learning Algorithms

这一奖项是为了支持一名博士后助理从事实验计算机科学工作。这位名叫Marcos Salganicoff的同事将与Ruzena Bajcsy博士合作，解决涉及机器人控制和规划的问题。这项研究将把学习、数据结构和并行性结合起来。规划信息将存储在二叉树(如k-D树或四叉树)中。这些信息通常是海量的，然后被用于学习算法。将采用一种混合方法，将分布式工作站用于部分计算，将MIMD计算机用于部分剩余计算，以成本效益的方式实施这些学习算法。

This award is to support a postdoctoral associate to work in experimental computer science. The associate, Marcos Salganicoff, will work with Dr. Ruzena Bajcsy on problems involving robot control and planning. The research will blend learning, data structures, and parallelism. Planning information will be stored in binary trees (such a k-D-trees or quadtrees). This information, usually massive amounts of it, is then used in learning algorithms. A hybrid approach using distributed workstations for some of the computation and a MIMD computer for some of the rest will be employed to implement these learning algorithms in a cost effective manner.