RI: Robot developmental learning of objects, actions, and tools

RI：机器人对物体、动作和工具的发展学习

• 批准号: 0713150
• 负责人: Benjamin Kuipers
• 金额: $ 45万
• 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: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0713150&HistoricalAwards=false
• 关键词: RI; Robot; developmental; learning; objects

Planning to achieve a goal requires knowledge of objects, actions, preconditions, and consequences. These abstract concepts are at a much higher level than the ''pixel-level'' sensory and motor interfaces between an embodied robot and the continuous world. Our goal is to show how high-level concepts of object and action can be learned autonomously from experience with low-level sensorimotor interaction. We hypothesize that these concepts are part of a larger package of foundational concepts that can be learned in approximately the following sequence: using motion to discriminate objects from background; detecting tight, reliable control loops to distinguish self from non-self objects; learning preconditions and consequences of actions applied to objects; identifying ''grasp'' actions that temporarily transform a non-self object to a self object; learning actions and effects that are achievable only with such an object (a tool!).The learning process depends on representing sensorimotor interaction with the world as a stochastic dynamical system. A ''curiosity'' drive rewards improvements in prediction reliability. Evaluation uses a simulated robot child with two arms, stereo vision, and a tray of blocks and other objects. This research will help robots learn their own high-level concepts, and could provide insights into human learning disabilities.

计划实现目标需要了解目标、行动、前提条件和后果。 这些抽象的概念是在一个更高的水平比“像素级”的感官和运动接口之间的体现机器人和连续的世界。 我们的目标是展示如何高层次的概念，对象和行动可以自主学习的经验与低层次的感觉运动的相互作用。 我们假设这些概念是一个更大的基本概念包的一部分，可以按照以下顺序学习：使用运动来区分物体和背景;检测紧密的，可靠的控制回路来区分自我和非自我物体;学习应用于物体的动作的先决条件和后果;识别暂时将非自我物体转换为自我物体的“抓取”动作;学习只有用这样的对象（工具！）才能实现的动作和效果。学习过程依赖于将感觉运动与世界的相互作用表示为随机动力系统。 “好奇心”驱动奖励预测可靠性的提高。 评估使用一个模拟的机器人儿童与两个手臂，立体视觉，和一个托盘的块和其他物体。 这项研究将帮助机器人学习自己的高级概念，并可能为人类学习障碍提供见解。