Learning the Sensorimotor Foundation for Spatial Reasoning

长寿命自主机器人的传感运动系统是其知识库的基础。这个项目解决的基本问题是，如何自主学习更高级别的特征和动作，并将其建立在与环境互动的经验基础上。这些特征和动作通常是由人类设计师设计成机器人的。该项目涉及三项任务。在任务1中，未知的感觉系统被分成不同的感觉“模式”，例如视觉和不同类型的距离传感器，目标是了解这些感知“模式”之间的区别、它们的个体属性以及传感器融合方法，以便将它们的信息组合成附近(“小规模”)环境的共同模型。在任务2中，“大尺度”环境的地图的基础被假设为一个确定的自动机模型，该模型由一组离散的所谓的“独特的状态”组成，这些状态通过动作可靠地联系在一起。一个目标是学习爬山控制规律，通过达到学习的特征探测器的局部最大激活来定义独特的状态。第二个目标是学习轨迹跟踪控制律，以便可靠地从一个独特的状态移动到另一个不同的状态，在那里爬山可以消除累积的误差。在任务3中，目标是学习对动态和静态环境进行建模，使用相干运动来区分单独的对象和背景，以便对它们进行分类，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要过滤掉的噪音的方法形成对比。同时，这些任务的结果将允许自主学习代理在自己的经验中固定自己的传感器和效应器，支持高水平的建模和与环境交互的能力。除了解决空间认知的基本问题外，这项研究在展示机器人如何自主适应新的或变化的传感器和效应器方面具有实际意义。这些成果可以应用于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中的非标准“机器人”，并可应用于为残疾人创造智能辅助工具，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，包括机器人学的创新本科课程，高中生参与该项目的工作，以及通过开放参观和公共演示向社区推广。该项目的重点之一是为行动和沟通障碍者开发智能轮椅，但认知和感知正常。

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.