Language to Action: Towards Interactive Task Learning with Physical Agents
Language to Action: Towards Interactive Task Learning with Physical Agents
复制标题
语言到行动：通过物理代理实现交互式任务学习
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Guangyue Xu
中科院分区：
文献类型：
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作者：
J. Chai;Qiaozi Gao;Lanbo She;Shaohua Yang;S. Saba;Guangyue Xu
语言交流在人类学习和知识获取中发挥着重要作用。随着新一代认知机器人的出现，让这些机器人直接向人类伙伴学习变得越来越重要。本文简要介绍了交互式任务学习，人类可以通过自然语言交流和动作演示来教授物理代理新任务。它讨论了在此过程中至关重要的语言和沟通基础方面的研究挑战和机遇。它进一步强调了常识知识，特别是非常基本的物理因果关系知识，在语言与感知和行动的基础上的重要性。
Language communication plays an important role in human learning and knowledge acquisition. With the emergence of a new generation of cognitive robots, empowering these robots to learn directly from human partners becomes increasingly important. This paper gives a brief introduction to interactive task learning where humans can teach physical agents new tasks through natural language communication and action demonstration. It discusses research challenges and opportunities in language and communication grounding that are critical in this process. It further highlights the importance of commonsense knowledge, particularly the very basic physical causality knowledge, in grounding language to perception and action.