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

计划实现一个目标需要了解目标、行动、前提条件和结果。这些抽象的概念比具体化的机器人和连续世界之间的“像素级”感觉和运动接口要高得多。我们的目标是展示物体和动作的高级概念如何从低级感官运动交互的经验中自主学习。我们假设这些概念是一个更大的基本概念包的一部分，这些基本概念可以按照大致如下的顺序学习：使用运动来区分对象和背景；检测紧密、可靠的控制环路以区分自我和非我对象；学习应用于对象的动作的前提条件和结果；识别将非我对象临时转换为自我对象的“抓取”动作；学习只有通过这样的对象(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交互表示为随机动态系统。“好奇心”的驱动力会奖励预测可靠性的提高。评估使用了一个模拟的机器儿童，有两只手臂，立体视觉，以及一盘积木和其他物体。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。

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.