Back to the Blocks World: Learning New Actions through Situated Human-Robot Dialogue
Back to the Blocks World: Learning New Actions through Situated Human-Robot Dialogue
复制标题
回到积木世界：通过情境人机对话学习新动作
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
N. Xi
中科院分区：
文献类型：
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作者：
Lanbo She;Shaohua Yang;Yu Cheng;Yunyi Jia;J. Chai;N. Xi
本文描述了一种方法，机器人手臂学习新的动作，通过对话在一个简化的方块世界。特别是，我们已经开发了一个三层的行动知识表示，一方面，支持语言的符号表示和机器人的连续感觉运动表示之间的连接;另一方面，支持现有的规划算法的应用程序，以解决新的情况。我们的实证研究表明，基于这种表示，机器人能够学习和执行块世界中的基本动作。当人类参与对话来教机器人新的动作时，与一次性指令相比，分步指令会带来更好的学习性能。
This paper describes an approach for a robotic arm to learn new actions through dialogue in a simplified blocks world. In particular, we have developed a threetier action knowledge representation that on one hand, supports the connection between symbolic representations of language and continuous sensorimotor representations of the robot; and on the other hand, supports the application of existing planning algorithms to address novel situations. Our empirical studies have shown that, based on this representation the robot was able to learn and execute basic actions in the blocks world. When a human is engaged in a dialogue to teach the robot new actions, step-by-step instructions lead to better learning performance compared to one-shot instructions.