Going Cognitive: A Demonstration of the Utility of Task-General Cognitive Architectures for Adaptive Robotic Task Performance
Tyler Frasca, Zhao Han, Jordan Allspaw, Holly Yanco, Matthias Scheutz
Abstract
It has been claimed that a main advantage of cognitive architectures (compared to other types of specialized robotic architectures) is that they are task-general and can thus learn to perform any task as long as they have the right perceptual and action primitives. In this paper, we provide empirical evidence for this claim by directly comparing a high-performing custom robotic architecture developed for the standardized robotic "FetchIt!" challenge task to a hybrid cognitive robotic architecture that allows for online one-shot task learning and task modifications through natural language instructions. The results show that there is no disadvantage of running the hybrid architecture (i.e., no significant difference in overall performance or computational overhead compared to the custom architecture) while adding the flexibility of online one-shot task instruction and modification not available in the custom architecture.
BibTeX
@inproceedings{iros2020_goingcognitivead,
title = {Going Cognitive: A Demonstration of the Utility of Task-General Cognitive Architectures for Adaptive Robotic Task Performance},
author = {Tyler Frasca and Zhao Han and Jordan Allspaw and Holly Yanco and Matthias Scheutz},
booktitle = {IROS 2020},
year = {2020}
}