Masih Haseli

Real-Time Learning of Predictive Dynamic Obstacle Models for Robotic Motion Planning

Autonomous systems often must predict the motions of nearby agents from partial and noisy data. This paper asks and answers the question: "Can we learn, in real-time, a nonlinear predictive model of another agent's motions?" Our online framework denoises and forecasts such dynamics using a modified …