Expressing Diverse Human Driving Behavior with Probabilistic Rewards and Online Inference
Liting Sun,Zheng Wu,Hengbo Ma,Masayoshi Tomizuka,Liting Sun,Zheng Wu,Hengbo Ma,Masayoshi Tomizuka
In human-robot interaction (HRI) systems, such as autonomous vehicles, understanding and representing human behavior are important. Human behavior is naturally rich and diverse. Cost/reward learning, as an efficient way to learn and represent human behavior, has been successfully applied in many domains. Most of traditional inverse reinforcement learning (IRL) algorithms, however, cannot adequatel...
