Natural Language Interaction to Facilitate Mental Models of Remote Robots
This addresses the challenge of human-machine teaming for novice operators in critical environments like emergency response, though it appears incremental as it builds on existing interaction methods.
The paper tackles the problem of unclear mental models among operators of remote robots in high-stakes scenarios by proposing a conversational assistant to provide natural language explanations, aiming to improve understanding and transparency.
Increasingly complex and autonomous robots are being deployed in real-world environments with far-reaching consequences. High-stakes scenarios, such as emergency response or offshore energy platform and nuclear inspections, require robot operators to have clear mental models of what the robots can and can't do. However, operators are often not the original designers of the robots and thus, they do not necessarily have such clear mental models, especially if they are novice users. This lack of mental model clarity can slow adoption and can negatively impact human-machine teaming. We propose that interaction with a conversational assistant, who acts as a mediator, can help the user with understanding the functionality of remote robots and increase transparency through natural language explanations, as well as facilitate the evaluation of operators' mental models.