In this paper, researchers explore a novel approach to reducing the burden on humans when demonstrating tasks to robots. The proposed method leverages automated excuses to guide the robot’s learning process, allowing the human operator to focus on the most critical aspects of the task rather than repeating entire demonstrations.
The authors define an excuse as a means of providing open-ended feedback for fixing issues with a planning task. In this context, an excuse is used to address the issue instead of the original goal. This approach allows the robot to learn from the demonstration while reducing the load on the human operator.
To evaluate the effectiveness of their proposed method, the researchers conducted a user study. The results showed that the fraction of useful demonstrations (i.e., the portion of the guided demonstration that is actually helpful) was significantly higher when using automated excuses compared to traditional demonstration methods.
In practical terms, the authors explain that even after removing redundant actions from the demonstration, there will still be parts of the guided demonstration that are not useful for the robot’s learning process. By measuring the fraction of useful demonstrations, they aim to identify the most effective way to reduce the burden on humans while still ensuring the robot learns efficiently.
The authors also acknowledge the limitations of their approach, including the potential for humans to misunderstand or misuse the automated excuses. They plan to address these challenges in future work.
In conclusion, this paper presents a promising solution for reducing the burden on humans when demonstrating tasks to robots. By leveraging automated excuses, the proposed method has the potential to improve the efficiency and effectiveness of the learning process while minimizing the load on human operators.
Automated Excuse Generation in Guided Demonstrations
