Exploiting 'Like Me' Hypotheses in Learning Robots

利用“像我一样”的假设来学习机器人乔什·邦加德***佛蒙特大学计算机科学系**华盛顿大学学习与脑科学研究所目前所有的机器人和计算机技术都必须由人工编程：这样的机器不仅必须由人类告诉它做什么，而且必须确切地告诉它如何去做。在这里，有人提议建造一种机器人设备，它可以通过观察人类或机器人老师来自主学习，推断老师想要做什么，然后设计不同的或更好的行动来实现老师的预期结果。这超越了简单的复制，即机器人复制老师的精确动作。这个机器人将首先创建自己和老师的内部模拟。然后，它将使用这些模拟来确定教师是如何移动的，以及这些移动会产生什么物理后果。最后，机器人可以利用自身的模拟来找到一种新的移动方式，以达到相同的目标。例如，人类老师可能会试图举起重物，但失败了。机器人会推断出老师的意图，并用它的抓手以另一种方式举起物体。机器人技术已经彻底改变了重工业，因为这种机器可以在一个结构化的环境中重复执行相同的动作，比如工厂车间。同样，计算机通过自动化那些可以以重复的方式执行的方面，已经彻底改变了人类社会的大多数部门。然而，在室外和非结构化的环境中——建筑工地、家庭、农场和其他星球的表面——机器将有同样或更大的用处，这些环境不断变化，因此挑战机器不断改变其完成给定任务的方式。通过观察其他人，机器人可以自己学习该做什么（推断老师的目标）和如何做（创造达到预期结果的新行为）。成功可能会导致智能设备能够在日常生活中与人类一起工作。这项工作还将有助于我们理解人类大脑是如何形成“自我”和“他人”意识的，以及这如何支持社会互动和交流中的“共同点”。这项工作还将影响我们对人类认知的基本机制的理解，即观察学习，通过阐明学生如何从简单的死记硬背到发现改进教师演示的解决方案的新解决方案。

Exploiting 'Like Me' Hypotheses for Learning RobotsJosh Bongard* & Andrew Meltzoff***Department of Computer Science, University of Vermont **Institute for Learning & Brain Sciences, University of WashingtonAll current robotic and computer technologies must be programmed by hand: such machines must not only be told what to do by a human, but how exactly to do it. Here it is proposed to construct a robotic device that can learn on its own by observing a human or robot teacher, inferring what that teacher is attempting to do, and then devising different or better actions to achieve the teacher's intended result. This surpasses simple copying, in which a robot reproduces the exact actions of a teacher. The proposed robot will first create internal simulations of itself and its teacher. It will then use these simulations to determine how the teacher is moving, and what physical consequences those movements will have. Finally, the robot can use the simulation of itself to find a new way of moving that will achieve the same goals. For instance, a human teacher may attempt and fail to lift a heavy object. The robot would infer the teacher's intent and use its grippers to lift the object in another way.Robotics has completely transformed heavy industry because such machines can perform the same actions repeatedly in a structured environment, such as a factory floor. Similarly, computers have revolutionized most sectors of human society by automating those aspects of them that can be performed in a repetitive manner. However, machines would be of equal or greater use in outdoor and unstructured environments--construction sites, homes, farms, and the surface of other planets--which constantly change, and therefore challenge the machine to continually alter how it achieves a given task. By observing others, a robot could learn both what to do (inferring the teacher's goals) and how to do it (creating new behaviors that achieve the intended result) on its own. Success could lead to intelligent devices capable to working alongside humans in the everyday world. The work would also contribute to our understanding of how the human brain develops a sense of 'self' and 'other' and how that supports social interaction and 'common ground' in communication. This work would also impact our understanding of a fundamental mechanism in human cognition, observational learning, by illuminating how students progress from simple rote copying to discovering novel solutions that improve upon solutions demonstrated by a teacher.