Abstract In most classical Autonomous Vehicle (AV) stacks, the prediction and planning
layers are separated, limiting the planner to react to predictions that are not informed by the …

This letter tackles the problem of robots collaboratively towing a load with cables to a
specified goal location while avoiding collisions in real time. The introduction of cables (as …

Offline reinforcement learning (RL) provides a framework for learning decision-making from
offline data and therefore constitutes a promising approach for real-world applications such …

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 …

Many autonomous agents, such as intelligent vehicles, are inherently required to interact
with one another. Game theory provides a natural mathematical tool for robot motion …

We propose an extension to the MAPF formulation, called SocialMapf, to account for private
incentives of agents in constrained environments such as doorways, narrow hallways, and …

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

We present an approach to ensure safe and deadlock-free navigation for decentralized multi-
robot systems operating in constrained environments, including doorways and intersections …

This paper presents a fully decentralized approach for realtime non-cooperative multi-robot
navigation in social mini-games, such as navigating through a narrow doorway or …

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 …