Abstract
This research introduces a novel framework for safe human-robot interaction that combines active human state estimation, conformal trajectory prediction, and Model Predictive Control. The framework enables robots to maintain probabilistic safety guarantees while adapting to human internal states such as attention levels and driving styles. Through simulations of diverse driving scenarios, we developed a reward-based human behavior model. This approach advances human-robot interaction by providing an adaptive method for safe robot planning that explicitly accounts for human behavioral uncertainties.
Document Type
Article
Class Name
Electrical and Systems Engineering Undergraduate Research
Language
English (en)
Date of Submission
12-4-2024
Recommended Citation
Wang, Haoyi, "Safe Robot Planning Through Understanding Human Behaviors" (2024). Electrical and Systems Engineering Undergraduate and Graduate Research. 31.
https://openscholarship.wustl.edu/eseundergraduate_research/31