Exploiting 'Like Me' Hypotheses in Learning Robots

在学习机器人中利用“像我一样”的假设

• 批准号: 0751385
• 负责人: Joshua Bongard
• 金额: --
• 依托单位: University of Vermont & State Agricultural College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-10-01 至 2010-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0751385&HistoricalAwards=false
• 关键词: Exploiting; Like; Me; Hypotheses; Learning

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.

Josh Bongard*& Andrew Meltzoff* 佛蒙特大学计算机科学系 ** 华盛顿大学学习脑科学研究所&所有当前的机器人和计算机技术都必须手工编程：这样的机器不仅必须由人类告诉做什么，而且必须准确地告诉如何做。这里提出了构建一种机器人设备，该机器人设备可以通过观察人类或机器人教师，推断教师试图做什么，然后设计出不同的或更好的行动来达到教师的预期效果。这超越了简单的复制，即机器人复制教师的精确动作。拟议中的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，以及这些移动将产生什么样的物理后果。最后，机器人可以利用对自身的模拟来找到一种新的移动方式，以实现相同的目标。例如，一个人类教师可能试图举起一个重物，但失败了。机器人会推断出老师的意图，然后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重复执行的方面，彻底改变了人类社会的大多数部门。然而，机器在户外和非结构化环境中的用途将是相同或更大的-建筑工地，家庭，农场和其他行星的表面-这些环境不断变化，因此挑战机器不断改变它如何完成给定的任务。通过观察其他人，机器人可以自己学习做什么（推断老师的目标）和如何做（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一起工作。这项工作也将有助于我们理解人类大脑如何发展“自我”和“他人”的意识，以及如何支持社会互动和沟通中的“共同点”。这项工作也将影响我们对人类认知的基本机制的理解，观察学习，通过阐明学生如何从简单的死记硬背到发现新的解决方案，改进教师演示的解决方案。