Language to Action: Towards Interactive Task Learning with Physical Agents
Language to Action: Towards Interactive Task Learning with Physical Agents
Joyce Y. Chai, Qiaozi Gao, Lanbo She, Shaohua Yang, Sari Saba-Sadiya, Guangyue Xu
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Invited Speakers. Pages 2-9.
https://doi.org/10.24963/ijcai.2018/1
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.
Keywords:
Natural Language Processing: Natural Language Semantics
Humans and AI: Human-AI Collaboration
Multidisciplinary Topics and Applications: Multidisciplinary Topics and Applications
Robotics: Human Robot Interaction