Exploiting 'Like Me' Hypotheses in Learning Robots

利用“像我一样”的假说学习机器人乔希·邦加德*和安德鲁·梅尔佐夫*佛蒙特大学计算机科学系**华盛顿大学脑科学研究所学习和推广所有当前的机器人和计算机技术都必须手动编程：这样的机器不仅必须由人类来告诉它做什么，而且必须确切地告诉它怎么做。在这里，有人提议构建一种机器人设备，通过观察人类或机器人教师，推断该教师试图做什么，然后设计不同的或更好的行动来实现教师的预期结果，从而实现自主学习。这超过了简单的复制，在复制中，机器人复制老师的确切动作。提出的机器人将首先创建自己和它的老师的内部模拟。然后，它将使用这些模拟来确定教师如何移动，以及这些移动将产生什么物理后果。最后，机器人可以利用对自己的模拟来找到一种新的移动方式，以实现相同的目标。例如，人类教师可能试图举起重物，但失败了。机器人会推断老师的意图，并用它的抓手以另一种方式举起物体。机器人已经彻底改变了重工业，因为这种机器可以在结构化的环境中重复执行相同的动作，比如工厂车间。同样，计算机通过将其可重复执行的那些方面自动化，从而彻底改变了人类社会的大多数部门。然而，机器将在户外和非结构化环境中发挥同等或更大的作用--建筑工地、家庭、农场和其他星球的表面--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。通过观察他人，机器人既可以学习做什么(推断老师的目标)，也可以学习如何做(创造新的行为来实现预期的结果)。成功可能会导致智能设备能够在日常世界中与人类并肩工作。这项工作还将有助于我们理解人类大脑是如何发展出一种“自我”和“他人”的感觉，以及它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一种基本机制-观察性学习的理解，通过阐明学生如何从简单的死记硬背发展到发现新的解决方案，这些解决方案在老师展示的解决方案的基础上有所改进。

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.