My research leverages non-expert feedback for the application of formal methods to computational human-robot interaction, with the aim of developing new ways to automatize robot failure corrections.
Correcting Robot FailuresPreviously, we have examined if/how people can identify and correct robot failures so that robots can continue their task even after failure (van Waveren, 2022). We found that people are able to identify and correct failures using natural language. This makes sense, because natural language is an intuitive means of providing feedback or input.
Driving styles play a major role in the acceptance and use of autonomous vehicles. Yet, existing motion planning techniques can often only incorporate simple driving styles that are modeled by the developers of the planner and not tailored to the passenger. We present a new approach to encode human driving styles through the use of signal temporal logic and its robustness metrics. Specifically, we use a penalty structure that can be used in many motion planning frameworks, and calibrate its parameters to model different automated driving styles.