Human Motion Prediction
Summary
Human motion prediction is a critical component of autonomous systems that interact with humans, enabling safe planning and decision-making. This subtopic focuses on developing robust and adaptive methods to predict human behavior in various contexts. Recent research has explored the use of Hamilton-Jacobi reachability-based frameworks to address challenges such as misspecified models and incorrect priors. These approaches aim to balance safety and efficiency by leveraging online human behavioral data to refine predictions while maintaining robustness. Compared to traditional stochastic predictors or worst-case forward reachable sets, reachability-based methods offer a more nuanced approach that can adapt to observations and provide continuous state and time predictions. Such advancements in human motion prediction contribute to the development of safer and more effective autonomous systems that can operate reliably in human-populated environments.