Michael T. Wolf — accepted AI-conference papers

2023

Congestion Prediction for Large Fleets of Mobile Robots

ICRA 2023poster

This paper introduces a deep learning (DL) approach to predicting congestion delays in large multi-robot systems. The problem is motivated by real-world problems in modern logistics automation, such as a warehouse with hundreds to thousands of coordinated mobile robots. Here, the large scale, the co…

Cited by 11 Source ↗Scholar ↗

2021

Fast Near-Optimal Heterogeneous Task Allocation via Flow Decomposition

Kiril Solovey, Saptarshi Bandyopadhyay, Federico Rossi, Michael T. Wolf, Marco Pavone

ICRA 2021poster

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 significantly higher performance than single-robot systems. A fundamental building block in most multi-robot systems is task al…

Cited by 3 Source ↗code ↗Scholar ↗