Bo Fu
Bo Fu
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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.
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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.
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