US-France (INRIA) Cooperative Research Robot World Representation, Interpretation, and Control

该奖项将支持美国和法国的一个合作研究项目，该项目专注于机器人学中的两个非常重要的问题：室内移动机器人和灵巧操作。参与这一联合项目的研究人员是犹他大学的托马斯·亨德森、马萨诸塞大学的埃德·里斯曼和法国国家信息科学与自动化研究所(INRIA)的奥利弗·福吉拉斯-索菲亚·安提波利斯。拟议的合作努力将允许研究一种新的方法来规范机器人的行为和控制。开发用于机器人控制的复杂系统并发现其组织原理需要多方面的研究工作，其组件由基于已证实的理论的重要和健壮的模块组成。参与研究的三个团队都在基于行为的机器人控制领域做出了重大的理论和工程贡献。犹他大学提供逻辑传感器和确定性同步有限状态自动机方面的专业知识；马萨诸塞州大学提供编程语言和环境、机器人控制和低层视觉方面的专业知识；INRIA提供运动分析、3D表示和实现试验台方面的专业知识。这个位于法国的组织是致力于机器人视觉的欧洲最强大的组织之一。这个联合项目将专注于逻辑行为的研究，这些行为表征了在代表机器人世界的语义网络中满足强加关系(即任务)的技术。这一合作努力的结果应该对我们理解移动机器人的行为做出重大贡献。

This award will support a U.S.-French collaborative research program focused on two highly important problems in robotics: indoor mobile robots and dexterous manipulation. The researchers involved in this joint project are Thomas Henderson, University of Utah, Ed Riseman, University of Massachusetts and Oliver Faugeras, French National Institute for Information Science and Automation (INRIA) - Sophia Antipolis. The proposed collaborative effort will permit the investigation of a new approach for the specification of robot behavior and control. The development of complex systems for robot control and the discovery of the principles of their organization require a multifaceted research effort whose components are comprised of significant and robust modules based on proven theory. The three involved research teams each have made significant theoretical and engineering contributions in the area of behavior-based robot control. The University of Utah provides expertise in logical sensors and deterministic synchronous Finite State Automata; the University of Massachusetts provides expertise in programming languages and environments, robot control land low-level vision, and INRIA provides expertise in motion analysis, 3D representation and implementation testbeds. The group in France is one of the strongest European groups working on robot vision. This joint project will be focused on research on logical behaviors which characterize techniques for the satisfaction of imposed relations (i.e. tasks) in a semantic network representing the robot's world. The results of this collaborative effort should make a significant contribution to our understanding of mobile robot behavior.