SGER: A Simulation Platform for Research on Developmental Robotics

SGER：发育机器人研究仿真平台

• 批准号: 0750011
• 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: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2009-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0750011&HistoricalAwards=false
• 关键词: SGER; Simulation; Platform; Research; Developmental

Proposal 0750011"A Simulation Platform, for Research on Developmental Robotics"PI: Benjamin J. KuipersUniversity of Texas at AustinABSTRACTThe goal of this project is to investigate the development of high-level concepts about objects and actions from low-level sensor input data. The project will take a developmental robotics approach: that is, it will use AI/robot models to investigate how concepts can be learned from an agent's own sensorimotor experience with its world. While it is possible to build some of this knowledge by hand, such knowledge tends to be incomplete and short-lived. Thus, for an autonomous agent to cope truly robustly with the complexity and diversity of real-world situations--and to be able to do so in more than short-lived robotic experiments--it is imperative for it to be able to use its own understanding of rich sensory input and motor actions to build its own models and concepts. In this paradigm, learning initiates with basic developmental learning to acquire and ground high-level concepts and then continues with life-long learning to adapt to changes in the world and in the robot's own capabilities. The initial thrust of this project will be to create a simulated baby robot model, which will then be evaluated. Once the baby robot is successful, the project will investigate whether the same methods will allow creation of sufficiently high-fidelity simulated models of apes or corvids (crows).

提案0750011“一个仿真平台，用于研究发展机器人“PI：本杰明J. Kuipers德克萨斯大学奥斯汀分校摘要本项目的目标是研究从低层次的传感器输入数据中发展关于物体和动作的高层次概念。该项目将采用发展机器人方法：也就是说，它将使用人工智能/机器人模型来研究如何从智能体自己的感觉运动经验中学习概念。虽然有可能手工建立一些知识，但这些知识往往是不完整和短暂的。因此，对于一个自主智能体来说，要真正强大地科普现实世界的复杂性和多样性，并且能够在短暂的机器人实验中做到这一点，它必须能够利用自己对丰富的感官输入和运动动作的理解来构建自己的模型和概念。在这种范式中，学习始于基本的发展性学习，以获得和巩固高级概念，然后继续终身学习，以适应世界和机器人自身能力的变化。该项目的最初目标是创建一个模拟婴儿机器人模型，然后对其进行评估。一旦婴儿机器人获得成功，该项目将研究同样的方法是否可以创建足够高保真的猿类或鸦类（乌鸦）模拟模型。