A System For Robot Concept Learning Through Situated Dialogue
Benjamin Kane, Felix Gervits, Matthias J. Scheutz, Matthew Marge · 2022
Robots operating in unexplored environments with human teammates will need to learn unknown concepts on the fly.To this end, we demonstrate a novel system that combines a computational model of question generation with a cognitive robotic architecture.The model supports dynamic production of backand-forth dialogue for concept learning given observations of an environment, while the architecture supports symbolic reasoning, action representation, one-shot learning and other capabilities for situated interaction.The system is able to learn about new concepts including objects, locations, and actions, using an underlying approach that is generalizable and scalable.We evaluate the system by comparing learning efficiency to a human baseline in a collaborative reference resolution task and show that the system is effective and efficient in learning new concepts, and that it can informatively generate explanations about its behavior. * Work performed during a summer position at the Army Research Laboratory.1 The term 'concept' in this paper refers to any entity in the task domain, including objects, locations, and actions.. . .