RI: Small: Robot Developmental Learning of Skilled Actions

RI：小：机器人技能动作的发展学习

• 批准号: 1421168
• 负责人: Benjamin Kuipers
• 金额: $ 44.68万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421168&HistoricalAwards=false
• 关键词: RI; Small; Robot; Developmental; Learning

The goal of this project is to show how a robot --- using a continuous stream of visual and tactile data --- can learn to work at a human level of skill in tasks normally done by humans. To function at a human level, it must be able to plan with "object-level" abstractions such as putting a red block into the box, and it must also be able to grasp objects and move them while avoiding bumping into things and causing damage to its surroundings. This project is inspired by human cognitive development. A baby learns about objects and actions by bootstrapping from early regularities and unreliable actions to hierarchies of more complex and reliable actions. The hypothesis to be tested is that this bootstrap learning approach allows a robot to achieve human levels of skillful and robust action in a wide range of human-dominated environments.This project draws on extensive prior work on foundational knowledge representations and machine learning. Learning begins by detecting low-level contingencies --- regularities among observed events --- and refining them into increasingly accurate predictive rules, that can be used to define reliable actions. For a given rule, a simple MDP model is formulated, and reinforcement learning methods learn a policy for accomplishing an action at the next level of the action hierarchy. Learned actions are initially unreliable, but policies and actions improve with experience. Attention is focused where learning is likely to be most productive by intrinsic motivation methods that reward actions that result in successful learning, including the important special case of rewarding attempts to imitate the successful actions of other agents.

这个项目的目标是展示一个机器人-使用一个连续的视觉和触觉数据流-如何学习在人类技能水平上工作，这些任务通常由人类完成。 要在人类层面上发挥作用，它必须能够使用“对象级别”的抽象进行规划，例如将红色块放入盒子中，并且还必须能够抓住物体并移动它们，同时避免撞到东西并对周围环境造成损害。 这个项目受到人类认知发展的启发。 一个婴儿通过从早期的不可靠和不可靠的动作到更复杂和可靠的动作层次的引导来学习物体和动作。 要测试的假设是，这种自举学习方法允许机器人在广泛的人类主导的环境中实现人类水平的熟练和鲁棒的动作。 学习从检测低水平的偶然事件开始--观察到的事件中的偶然事件--并将它们提炼成越来越准确的预测规则，这些规则可用于定义可靠的行动。 对于给定的规则，制定一个简单的MDP模型，强化学习方法学习一个策略，用于在动作层次结构的下一级完成动作。 学习的行动最初是不可靠的，但政策和行动会随着经验而改进。 注意力集中在学习可能是最富有成效的内在激励方法，奖励行动，导致成功的学习，包括奖励尝试模仿其他代理人的成功行动的重要特殊情况。