Experience-based representation construction: learning from human and robot teachers
Experience-based representation construction: learning from human and robot teachers
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DOI：
10.1109/iros.2001.976257
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发表时间：
2001-10
期刊：
影响因子：
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通讯作者：
M. Nicolescu;M. Matarić
中科院分区：
文献类型：
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作者：
M. Nicolescu;M. Matarić
在本文中，我们解决的问题，教机器人执行各种任务。我们提出了一种基于行为的方法，扩展了机器人的能力，使他们能够从自己与人类互动的经验中学习复杂任务的表示，并使用所获得的知识来教其他机器人。学习机器人跟随人类或机器人教师，并将自己对环境的观察映射到其内部行为，在运行时以行为网络的形式构建经验任务的表示。为了实现这一点，我们引入了一个架构，允许表示和执行复杂和灵活的行为序列和一个在线算法，建立任务表示从观察。我们展示了我们的方法在一组人（教师）-机器人（学习者）和机器人（教师）-机器人（学习者）的实验中，其中机器人学习表示多个任务，并能够执行它们，即使在环境中的干扰对象，可能会阻碍学习和执行过程。
In this paper we address the problem of teaching robots to perform various tasks. We present a behavior-based approach that extends the capabilities of robots, allowing them to learn representations of complex tasks from their own experiences of interacting with a human, and to use the acquired knowledge to teach other robots in turn. A learner robot follows a human or robot teacher and maps its own observations of the environment to its internal behaviors, building at run-time a representation of the experienced task in the form of a behavior network. To enable this, we introduce an architecture that allows the representation and execution of complex and flexible sequences of behaviors and an online algorithm that builds the task representation from observations. We demonstrate our approach in a set of human(teacher)-robot(learner) and robot(teacher)-robot(learner) experiments, in which the robots learn representations for multiple tasks and are able to execute them even in environments with distractor objects that could hinder the learning and the execution process.