Learning the Sensorimotor Foundation for Spatial Reasoning

学习空间推理的感觉运动基础

• 批准号: 0413257
• 负责人: Benjamin Kuipers
• 金额: --
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0413257&HistoricalAwards=false
• 关键词: Learning; Sensorimotor; Foundation; Spatial; Reasoning

The sensorimotor system for a long-lived autonomous robot is the foundation for its knowledge base. This project addresses fundamental questions about how higher-level features and actions, which are usually engineered into robots by human designers, can be learned autonomously and grounded in experience interacting with the environment. The project involves three tasks. In Task 1, an unknown sensory system is divided into different sensory "modalities", for example, vision and different types of range sensors, and the goal is to learn the distinctions among these, their individual properties, and sensor fusion methods for combining their information into a common model of the nearby ("small-scale'') environment. In Task 2, the basis for a map of the "large-scale'' environment is assumed to be a deterministic automaton model consisting of a discrete set of so-called "distinctive states" that are reliably connected by actions. One goal is to learn hill-climbing control laws that define distinctive states by reaching the local maximum activation of learned feature-detectors. A second goal is to learn trajectory-following control laws for moving reliably from one distinctive state to the neighborhood of another, where hill-climbing eliminates accumulated error. In Task 3, the goal is to learn to model the dynamic as well as the static environment, using coherent motion to distinguish individual objects from the background, so they can be categorized and their properties learned to support recognition elsewhere. This is in contrast to the usual approach of assuming that the environment is static and that anything dynamic is treated as noise to be filtered out.Together, the results of these tasks will allow an autonomous learning agent to ground its own sensors and effectors in its own experience, supporting a high level of competence in modeling and interacting with its environment. Besides addressing fundamental issues in spatial cognition, this research has practical importance in showing how robots can adapt autonomously to new or changing sensors and effectors. These results can apply to non-standard "robots'' such as autonomic computing in complex computing systems, distributed sensor networks, MEMS sensors, or reconfigurable autonomous spacecraft, and have applications to the creation of intelligent aids, such as wheelchairs, for the disabled.The results of this project have broader impacts on many educational activities, including innovative undergraduate courses in robotics, involvement of high school students in the work of the project, and outreach to the community through open houses and public demonstrations. One focus of the project is the development of mobility aids like an Intelligent Wheelchair for persons with disabilities in mobility and communication, but with normal cognition and perception.

长寿命自主机器人的感觉运动系统是其知识库的基础。该项目解决了一些基本问题，即通常由人类设计师设计到机器人中的高级功能和动作如何能够自主学习，并以与环境交互的经验为基础。该项目涉及三项任务。在任务1中，一个未知的感觉系统被分为不同的感觉“模态”，例如，视觉和不同类型的距离传感器，目标是学习这些之间的区别，它们的个体属性，以及将它们的信息组合到附近（“小尺度”）环境的共同模型中的传感器融合方法。在任务2中，“大规模”环境地图的基础被假设为一个确定性自动机模型，由一组所谓的“独特状态”组成，这些状态通过动作可靠地连接在一起。 一个目标是学习爬山控制律，通过达到学习的特征检测器的局部最大激活来定义不同的状态。 第二个目标是学习随机跟随控制律，以便从一个独特的状态可靠地移动到另一个状态的邻域，其中爬山消除了累积误差。在任务3中，目标是学习对动态和静态环境进行建模，使用连贯的运动来区分单个对象和背景，因此可以对它们进行分类，并学习它们的属性以支持其他地方的识别。这与通常的假设环境是静态的，任何动态的东西都被视为噪音被过滤掉的方法相反，这些任务的结果将允许自主学习代理根据自己的经验建立自己的传感器和效应器，支持高水平的建模能力和与环境的交互。除了解决空间认知中的基本问题外，这项研究在展示机器人如何自主适应新的或不断变化的传感器和效应器方面具有重要的实际意义。这些结果可以应用于非标准的“机器人”，如复杂计算系统中的自主计算、分布式传感器网络、MEMS传感器或可重构的自主航天器，并应用于为残疾人创造智能辅助设备，如轮椅。该项目的结果对许多教育活动产生了更广泛的影响，包括机器人技术的创新本科课程，高中生参与项目工作，并通过开放日和公众示威向社区宣传。该项目的一个重点是为行动和交流方面有残疾但认知和知觉正常的人开发助行器，如智能轮椅。