News
- Jun 30, 2020
Our paper is accepted to the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), a top robotics conference in robotics! This is a collaboration project with Tufts University, the Human-Robot Interaction laboratory.
Contents
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.
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