Xiaofeng Lin

I am a first-year Systems Engineering Ph.D. student at Boston University where I work on reinforcement learning under the supervision of Prof. Xuezhou Zhang.

I earned my Master of Science in Robotics from University of Michigan, Ann Arbor, where I was fortunate to be advised by Prof. Vasileios Tzoumas. During my master, I worked on online learnig, submodular maximization and multi-agent systems.

I was a Research Assitant at Intelligent Unmanned Systems Laboratory, Westlake University under the supervision of Prof. Shiyu Zhao, where I worked on target tracking of drones and software development of groundstation.

I earned my B.S.E in Engineering Mechanics from Tianjin University.

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Research

I'm broadly interested in robotics, reinforcement learning, online learning. I aspire to enable robots to make better decisions in unstructured environments.

This paper studies the task of vision-based MAV-catching-MAV, where a catcher MAV can detect, localize, and pursue a target MAV autonomously. This paper proposes a real-to-sim-to-real approach and sucessfully implements a fully autonomous vision-based MAV-catching-MAV system.

The algorithm leverages a meta-algorithm to learn whether the robots should follow untrustworthy commands or a recently developed submodular coordination algorithm, Bandit Sequential Greedy (BSG), which has performance guarantees. The algorithm asymptotically can achieve the better performance out of the commands and the BSG algorithm.

The algorithm generalizes the seminal Sequential Greedy algorithm by Fisher et al. to the bandit setting, by leveraging submodularity and algorithms for the problem of tracking the best action. We validate our algorithm in simulated scenarios of multi-target tracking.

Thanks for Jon Barron for this amazing template.