156 citations · 255 across the 14 of their papers we have counts for
7 papers · 1 filter
Optimizing pre-scheduled, intermittently-observed MDPs
Patrick Zhong, Federico Rossi, Dylan A. Shell
A challenging category of robotics problems arises when sensing incurs substantial costs. This paper examines settings in which a robot wishes to limit its observations of state, f…
Planning under periodic observations: bounds and bounding-based solutions
Federico Rossi, Dylan Shell
We study planning problems faced by robots operating in uncertain environments with incomplete knowledge of state, and actions that are noisy and/or imprecise. This paper identifie…
Multi-Robot On-site Shared Analytics Information and Computing
Joshua Vander Hook, Federico Rossi, Tiago Vaquero +5
Computation load-sharing across a network of heterogeneous robots is a promising approach to increase robots capabilities and efficiency as a team in extreme environments. However,…
Fast Near-Optimal Heterogeneous Task Allocation via Flow Decomposition
Kiril Solovey, Saptarshi Bandyopadhyay, Federico Rossi +2
Multi-robot systems are uniquely well-suited to performing complex tasks such as patrolling and tracking, information gathering, and pick-up and delivery problems, offering signifi…
The Pluggable Distributed Resource Allocator (PDRA): a Middleware for Distributed Computing in Mobile Robotic Networks
Federico Rossi, Tiago Stegun Vaquero, Marc Sanchez Net +2
We present the Pluggable Distributed Resource Allocator (PDRA), a middleware for distributed computing in heterogeneous mobile robotic networks. PDRA enables autonomous robotic age…
Review of Multi-Agent Algorithms for Collective Behavior: a Structural Taxonomy
Federico Rossi, Saptarshi Bandyopadhyay, Michael Wolf +1
In this paper, we review multi-agent collective behavior algorithms in the literature and classify them according to their underlying mathematical structure. For each mathematical…
Data-Driven Model Predictive Control of Autonomous Mobility-on-Demand Systems
Ramon Iglesias, Federico Rossi, Kevin Wang +3
The goal of this paper is to present an end-to-end, data-driven framework to control Autonomous Mobility-on-Demand systems (AMoD, i.e. fleets of self-driving vehicles). We first mo…