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Nathalie Majcherczyk

7 accepted papers

2025

Reliable and Efficient Multi-Agent Coordination via Graph Neural Network Variational Autoencoders

ICRA 2025

Multi-agent coordination is crucial for reliable multi-robot navigation in shared spaces such as automated warehouses. In regions of dense robot traffic, local coordination methods may fail to find a deadlock-free solution. In these scenarios, it is appropriate to let a central unit generate a globa

Cited by 4SourceScholar
2025

Scalable Multi-Robot Task Allocation and Coordination Under Signal Temporal Logic Specifications

ICRA 2025

Motion planning with simple objectives, such as collision-avoidance and goal-reaching, can be solved efficiently using modern planners. However, the complexity of the allowed tasks for these planners is limited. On the other hand, signal temporal logic (STL) can specify complex requirements, but STL

Cited by 0SourceScholar
2023

Learning to View: Decision Transformers for Active Object Detection

ICRA 2023poster

Active perception describes a broad class of techniques that couple planning and perception systems to move the robot in a way to give the robot more information about the environment. In most robotic systems, perception is typically independent of motion planning. For example, traditional object de…

Cited by 18SourceScholar
2021

Distributed Data Storage and Fusion for Collective Perception in Resource-Limited Mobile Robot Swarms

RA-L 2021

In this letter, we propose an approach to the distributed storage and fusion of data for collective perception in resource-limited robot swarms. We demonstrate our approach in a distributed semantic classification scenario. We consider a team of mobile robots, in which each robot runs a pre-trained

Cited by 9SourceScholar
2021

Flow-FL: Data-Driven Federated Learning for Spatio-Temporal Predictions in Multi-Robot Systems

ICRA 2021poster

In this paper, we show how the Federated Learning (FL) framework enables learning collectively from distributed data in connected robot teams. This framework typically works with clients collecting data locally, updating neural network weights of their model, and sending updates to a server for aggr…

Cited by 50SourceScholar
2018

Decentralized Connectivity-Preserving Deployment of Large-Scale Robot Swarms

IROS 2018poster

We present a decentralized and scalable approach for deployment of a robot swarm. Our approach tackles scenarios in which the swarm must reach multiple spatially distributed targets, and enforce the constraint that the robot network cannot be split. The basic idea behind our work is to construct a l…

Cited by 36SourceScholar