Deep reinforcement learning (DRL) provides a promising way for learning navigation in
complex autonomous driving scenarios. However, identifying the subtle cues that can …

End-to-end approaches to autonomous driving commonly rely on expert demonstrations.
Although humans are good drivers, they are not good coaches for end-to-end algorithms …

Reliable trajectory planning like human drivers in real-world dynamic urban environments is
a critical capability for autonomous driving. To this end, we develop a vision and imitation …

With the development of deep representation learning, the domain of reinforcement learning
(RL) has become a powerful learning framework now capable of learning complex policies …

Imitation learning (IL) is a simple and powerful way to use high-quality human driving data,
which can be collected at scale, to produce human-like behavior. However, policies based …

Despite decades of research, existing navigation systems still face real-world challenges
when deployed in the wild, eg, in cluttered home environments or in human-occupied public …

Reinforcement learning is considered to be a strong AI paradigm which can be used to
teach machines through interaction with the environment and learning from their mistakes …

This letter presents a case study of a learning-based approach for target-driven mapless
navigation. The underlying navigation model is an end-to-end neural network, which is …

Navigating through unstructured environments is a basic capability of intelligent creatures,
and thus is of fundamental interest in the study and development of artificial intelligence …

While imitation learning for vision-based au-tonomous mobile robot navigation has recently
received a great deal of attention in the research community, existing approaches typically …