Reward function, as an incentive representation that recognizes humans' agency and
rationalizes humans' actions, is particularly appealing for modeling human behavior in …

In human-robot interaction (HRI) systems, such as autonomous vehicles, understanding and
representing human behavior are important. Human behavior is naturally rich and diverse …

Reinforcement learning (RL) has achieved tremendous success as a general framework for
learning how to make decisions. However, this success relies on the interactive hand-tuning …

Autonomous vehicles are sharing the road with human drivers. In order to facilitate
interactive driving and cooperative behavior in dense traffic, a thorough understanding and …

One main challenge to robot decision making in human-robot teams involves predicting the
intents of a human team member through observations of the human's behavior. Inverse …

The input of visual reinforcement learning often contains redundant information, which will
reduce the decision efficiency and decrease the performance of the agent. To address this …

Reinforcement learning has enabled agents to solve challenging tasks in unknown
environments. However, manually crafting reward functions can be time consuming …

We approach the problem of understanding how people interact with each other in
collaborative settings, especially when individuals know little about their teammates, via …

Reinforcement learning (RL) studies how an agent comes to achieve reward in an
environment through interactions over time. Recent advances in machine RL have …

Reinforcement learning (RL) agents optimize only the features specified in a reward function
and are indifferent to anything left out inadvertently. This means that we must not only …