7 accepted papers
Rapid urbanization has increased demand for customized urban mobility, making on-demand services and robo-taxis central to future transportation. The efficiency of these systems hinges on real-time fleet coordination algorithms. This work accelerates the state-of-the-art high-capacity ridepooling fr
Rapid urbanization has increased demand for customized urban mobility, making on-demand services and robo-taxis central to future transportation. The efficiency of these systems hinges on real-time fleet coordination algorithms. This work accelerates the state-of-the-art high-capacity ridepooling fr…
Large robot fleets are now common in warehouses and other logistics settings, where small control gains translate into large operational impacts. In this article, we address task scheduling for lifelong Multi-Agent Pickup-and-Delivery (MAPD) and propose a hybrid method that couples learning-based gl
The control of high-dimensional systems, such as soft robots, requires models that faithfully capture complex dynamics while remaining computationally tractable. This work presents a framework that integrates Graph Neural Network (GNN)-based dynamics models with structure-exploiting Model Predictive…
Hierarchical policies enable strong performance in many sequential decision-making problems, such as those with high-dimensional action spaces, those requiring long-horizon planning, and settings with sparse rewards. However, learning hierarchical policies from static offline datasets presents a si…
Model predictive control (MPC) has established itself as the primary methodology for constrained control, enabling general-purpose robot autonomy in diverse real-world scenarios. However, for most problems of interest, MPC relies on the recursive solution of highly non-convex trajectory optimization
Optimization problems over dynamic networks have been extensively studied and widely used in the past decades to formulate numerous real-world problems. However, (1) traditional optimization-based approaches do not scale to large networks, and (2) the design of good heuristics or approximation algor…