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James Paulos

13 accepted papers

2022

Coverage Control in Multi-Robot Systems via Graph Neural Networks

ICRA 2022poster

This paper develops a decentralized approach to mobile sensor coverage by a multi-robot system. We consider a scenario where a team of robots with limited sensing range must position itself to effectively detect events of interest in a region characterized by areas of varying importance. Towards thi…

Cited by 40SourceScholar
2021

Combined Routing and Scheduling of Heterogeneous Transport and Service Agents

IROS 2021poster

This paper investigates servicing waypoints in a wide area using collaborative deployments of vehicles with heterogeneous range and mobility constraints. We formulate a joint planning problem for a single transport truck and multiple service drones in which the truck is constrained to a road and mus…

Cited by 3SourceScholar
2021

Dispersion-Minimizing Motion Primitives for Search-Based Motion Planning

ICRA 2021poster

Search-based planning with motion primitives is a powerful motion planning technique that can provide dynamic feasibility, optimality, and real-time computation times on size, weight, and power-constrained platforms in unstructured environments. However, optimal design of the motion planning graph,…

Cited by 17SourceScholar
2021

Learning Connectivity for Data Distribution in Robot Teams

IROS 2021poster

Many algorithms for control of multi-robot teams operate under the assumption that low-latency, global state information necessary to coordinate agent actions can readily be disseminated among the team. However, in harsh environments with no existing communication infrastructure, robots must form ad…

Cited by 12SourcecodeScholar
2021

Multi-Robot Coverage and Exploration using Spatial Graph Neural Networks

IROS 2021poster

The multi-robot coverage problem is an essential building block for systems that perform tasks like inspection, exploration, or search and rescue. We discretize the coverage problem to induce a spatial graph of locations and represent robots as nodes in the graph. Then, we train a Graph Neural Netwo…

Cited by 79SourceScholar
2020

IMU-Based Inertia Estimation for a Quadrotor Using Newton-Euler Dynamics

RA-L 2020

In this letter, we demonstrate that a quadrotor's tilt, angular velocity, linear velocity and the parameters shown in Table II may be estimated using only an inertial measurement unit (IMU) and motor speed feedback for sensing. Motor speed commands are used to drive the process model and the motor s

Cited by 52SourceScholar
2020

Neurosymbolic Transformers for Multi-Agent Communication

NeurIPS 2020poster

We study the problem of inferring communication structures that can solve cooperative multi-agent planning problems while minimizing the amount of communication. We quantify the amount of communication as the maximum degree of the communication graph; this metric captures settings where agents have…

2019

Decentralization of Multiagent Policies by Learning What to Communicate

ICRA 2019poster

Effective communication is required for teams of robots to solve sophisticated collaborative tasks. In practice it is typical for both the encoding and semantics of communication to be manually defined by an expert; this is true regardless of whether the behaviors themselves are bespoke, optimizatio…

Cited by 36SourceScholar
2019

Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks

CoRL 2019

We consider the problem of finding distributed controllers for large networks of mobile robots with interacting dynamics and sparsely available communications. Our approach is to learn local controllers that require only local information and communications at test time by imitating the policy of ce