3 accepted papers
Efficient human-agent collaboration requires understanding each other’s capabilities and establishing appropriate reliance. My thesis focuses on optimizing performance in mixed-initiative settings, where humans and agents dynamically contribute to decisions and actions. I first explore key factors s…
The need for opponent modeling and tracking arises in several real-world scenarios, such as professional sports, video game design, and drug-trafficking interdiction. In this work, we present Graph based Adversarial Modeling with Mutual Information (GrAMMI) for modeling the behavior of an adversaria…
Learning from demonstration (LfD) seeks to democratize robotics by enabling non-experts to intuitively program robots to perform novel skills through human task demonstration. Yet, LfD is challenging under a task and motion planning setting which requires hierarchical abstractions. Prior work has st…