Extending recent advances in Learning from Demonstration (LfD) frameworks to multi-robot
settings poses critical challenges such as environment non-stationarity due to partial …

In this work, we develop a scalable, local tra-jectory optimization algorithm that enables
robots to interact with other robots. It has been shown that agents' interactions can be …

Contingency planning, wherein an agent generates a set of possible plans conditioned on
the outcome of an uncertain event, is an increasingly popular way for robots to act under …

Effectively predicting intent and behavior requires inferring leadership in multi-agent
interactions. Dynamic games provide an expressive theoretical framework for modeling …

Interactive behavior modeling of multiple agents is an essential challenge in simulation,
especially in scenarios when agents need to avoid collisions and cooperate at the same …

In this work, we consider the problem of autonomous racing with multiple agents where
agents must interact closely and influence each other to compete. We model interactions …

Although dynamic games provide a rich paradigm for modeling agents' interactions, solving
these games for real-world applications is often challenging. Many real-world interactive …

Game theoretic methods have become popular for planning and prediction in situations
involving rich multi-agent interactions. However, these methods often assume the existence …

As intelligent robots like autonomous vehicles become increasingly deployed in the
presence of people, the extent to which these systems should leverage model-based game …

Game theory offers an interpretable mathematical framework for modeling multi-agent
interactions. However, its applicability in real-world robotics applications is hindered by …