10 citations · 12 across the 9 of their papers we have counts for
10 papers
Trust-Aware Sequential Decision Making and Rollout Planning for Resilient Multi-Robot Systems
Roee M. Francos, Daniel Garces, Orhan Eren Akgün +2
Sequential decision-making in multi-robot systems typically assumes that planning information is reliable and that agents execute the actions anticipated by the planner. Compromise…
Model-Based Reinforcement Learning for Heterogeneous Multi-Robot Task Assignment Under Distribution Shifts
Daniel Garces, Sara Castro, Adrian Haimovich +2
Heterogeneous multi-robot service systems must assign requests to compatible robots, construct feasible schedules, and adapt as new tasks arrive online. Historical data can help an…
Operational Reliability of Deadline-Constrained Task Assignment: Stability Characterization and Adversarial Routing
Roee M. Francos, Daniel Garces, Orhan Eren Akgün +1
Automated task-assignment systems often serve stochastic tasks subject to finite deadlines. In these settings, conventional backlog-based stability can be misleading: finite task l…
Provably Stable Multi-Agent Routing with Bounded-Delay Adversaries in the Decision Loop
Roee M. Francos, Daniel Garces, Stephanie Gil
In this work, we are interested in studying multi-agent routing settings, where adversarial agents are part of the assignment and decision loop, degrading the performance of the fl…
Pro-Routing: Proactive Routing of Autonomous Multi-Capacity Robots for Pickup-and-Delivery Tasks
Daniel Garces, Stephanie Gil
We consider a multi-robot setting, where we have a fleet of multi-capacity autonomous robots that must service spatially distributed pickup-and-delivery requests with fixed maximum…
Data-Efficient Multi-Agent Spatial Planning with LLMs
Huangyuan Su, Aaron Walsman, Daniel Garces +2
In this project, our goal is to determine how to leverage the world-knowledge of pretrained large language models for efficient and robust learning in multiagent decision making. W…
Tiny Reinforcement Learning for Quadruped Locomotion using Decision Transformers
Orhan Eren Akgün, Néstor Cuevas, Matheus Farias +1
Resource-constrained robotic platforms are particularly useful for tasks that require low-cost hardware alternatives due to the risk of losing the robot, like in search-and-rescue…
Approximate Multiagent Reinforcement Learning for On-Demand Urban Mobility Problem on a Large Map (extended version)
Daniel Garces, Sushmita Bhattacharya, Dimitri Bertsekas +1
In this paper, we focus on the autonomous multiagent taxi routing problem for a large urban environment where the location and number of future ride requests are unknown a-priori,…
Surge Routing: Event-informed Multiagent Reinforcement Learning for Autonomous Rideshare
Daniel Garces, Stephanie Gil
Large events such as conferences, concerts and sports games, often cause surges in demand for ride services that are not captured in average demand patterns, posing unique challeng…
Multiagent Reinforcement Learning for Autonomous Routing and Pickup Problem with Adaptation to Variable Demand
Daniel Garces, Sushmita Bhattacharya, Stephanie Gil +1
We derive a learning framework to generate routing/pickup policies for a fleet of autonomous vehicles tasked with servicing stochastically appearing requests on a city map. We focu…