Bo Fu

I am an Applied Scientist at Amazon Robotics, where I develop advanced optimization and learning algorithms, as well as planning and simulation tools, to improve the efficiency and reliability of multi-robot systems in Amazon fulfillment centers. My current research interest is in decision making and path planning for multi-robot systems under uncertainty. 

I earned my Ph.D. in Robotics from the University of Michigan, where I conducted research with professors Kira Barton and Maani Ghaffari Jadidi. I hold a M.S. from Carnegie Mellon University where I worked with professor Nathan Michael on visual-inertial odometry systems for quadcopters. Before that, I studied vehicle engineering and had my undergraduate research focused on control strategies for hybrid electric vehicles.

News

Jan 16, 2024

Oct 3, 2023

Aug 25, 2023

Nov 10, 2022

May 23, 2022

Feb 1, 2022

Nov 11, 2021

Oct 25, 2020

Oct 13, 2020

 

I successfully defended my Ph.D. dissertation: A Learning and Planning Framework for Robust Task Allocation for Heterogeneous Robot Teams.

I gave a presentation at IROS 2023 (the content is the same as this video).

I completed an internship (Applied Scientist II Intern) at Amazon Robotics. The context of the system that I worked on is shown here.

A paper is published at IEEE Transactions on Robotics. Robust Task Scheduling for Heterogeneous Robot Teams Under Capability Uncertainty.

A workshop paper is presented at ICRA 2022 Workshop on Collaborative Robots and the Work of the Future.

A new paper published at Journal of Autonomous Vehicles and Systems - ASME. Simultaneous human-robot matching and routing for multi-robot tour guiding under time uncertainty.

A new co-authored paper presented at 2021 Winter Simulation Conference. Phoenix, AZ. Room match: Achieving thermal comfort through smart space allocation and environ- mental control in buildings.

A new paper presented at IROS 2020. Heterogeneous Vehicle Routing and Teaming with Gaussian Distributed Energy Uncertainty.

A new article about our project at the Automotive Research Center.