7 accepted papers
Recent progress in geospatial foundation models highlights the importance of learning general-purpose representations for real-world locations, particularly points-of-interest (POIs) where human activity concentrates. Existing approaches, however, focus primarily on place identity derived from stati…
We present a scalable planning algorithm for multi-agent sequential decision problems that require dynamic collaboration. Teams of agents need to coordinate decisions in many domains, but naive approaches fail due to the exponential growth of the joint action space with the number of agents. We c…
We consider the problem of dynamically allocating tasks to multiple agents under time window constraints and task completion uncertainty. Our objective is to minimize the number of unsuccessful tasks at the end of the operation horizon. We present a multi-robot allocation algorithm that decouples t…
We consider the problem of controlling a large fleet of drones to deliver packages simultaneously across broad urban areas. To conserve energy, drones hop between public transit vehicles (e.g., buses and trams). We design a comprehensive algorithmic framework that strives to minimize the maximum tim…
We consider the problem of computing shortest paths in a dense motion-planning roadmap G. We assume that n, the number of vertices of G, is very large. Thus, using any path-planning algorithm that directly searches G, running in O(VlogV + E) ≈ O(n2) time, becomes unacceptably expensive. We are there…
In human-robot collaborative tasks, incorporating qualitative information provided by humans can greatly enhance the robustness and efficacy of robot state estimation. We introduce an algorithmic framework to model qualitative information as quantitative constraints on and between states. Our approa…
We present POMP (Pareto Optimal Motion Planner), an anytime algorithm for geometric path planning on roadmaps. For robots with several degrees of freedom, collision checks are computationally expensive and often dominate planning time. Our goal is to minimize the number of collision checks for obtai…