Integrating kinematics and environment context into deep inverse reinforcement learning for predicting off-road vehicle trajectories

Understand

Predicting the motion of a mobile agent from a third-person perspective is an important component for many robotics applications, such as autonomous navigation and tracking.

• With accurate motion prediction of other agents, robots can plan for more intelligent behaviors to achieve specified objectives, instead of acting in a purely reactive way.
• Previous work addresses motion prediction by either only filtering kinematics, or using hand-designed and learned representations of the environment.
• Instead of separating kinematic and environmental context, we propose a novel approach to integrate both into an inverse reinforcement learning (IRL) framework for trajectory prediction.