Jinghan Peng

Multi-Task Invariant Representation Imitation Learning for Autonomous Driving

Imitation learning is a promising approach to acquiring autonomous driving policies by mimicking human driver behaviors. However, a major drawback of existing driving policies derived from imitation learning is their proneness to capturing spurious correlations, owing to the lack of an explicit caus