Peilun Li

Plug-and-Play Physics-Informed Learning Using Uncertainty Quantified Port-Hamiltonian Models

The ability to predict trajectories of surrounding agents and obstacles is a crucial component in many robotic applications. Data-driven approaches are commonly adopted for state prediction in scenarios where the underlying dynamics are unknown. However, the performance, reliability, and uncertainty