Name
Title | ||
|---|---|---|
Explanation-Based Reward Coaching to Improve Human Performance via Reinforcement Learning |
Abstract | ||
|---|---|---|
For robots to effectively collaborate with humans, it is critical to establish a shared mental model amongst teammates. In the case of incongruous models, catastrophic failures may occur unless mitigating steps are taken. To identify and remedy these potential issues, we propose a novel mechanism for enabling an autonomous system to detect model disparity between itself and a human collaborator, infer the source of the disagreement within the model, evaluate potential consequences of this error, and finally, provide human-interpretable feedback to encourage model correction. This process effectively enables a robot to provide a human with a policy update based on perceived model disparity, reducing the likelihood of costly or dangerous failures during joint task execution. This paper makes two contributions at the intersection of explainable AI (xAI) and human-robot collaboration: 1) The Reward Augmentation and Repair through Explanation (RARE) framework for estimating task understanding and 2) A human subjects study illustrating the effectiveness of reward augmentation-based policy repair in a complex collaborative task.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/HRI.2019.8673104 | 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI) |
Keywords | Field | DocType |
Task analysis,Robots,Collaboration,Maintenance engineering,Hidden Markov models,Cognitive science,Team working | Mental model,Task analysis,Computer science,Human–computer interaction,Coaching,Autonomous system (mathematics),Robot,Hidden Markov model,Maintenance engineering,Reinforcement learning | Conference |
ISSN | ISBN | Citations |
2167-2121 | 978-1-5386-8555-6 | 1 |
PageRank | References | Authors |
0.36 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aaquib Tabrez | 1 | 1 | 1.71 |
| Shivendra Agrawal | 2 | 1 | 0.36 |
| Bradley Hayes | 3 | 1 | 1.04 |