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Christopher D. Hsu

2 accepted papers

2024

Active Scout: Multi-Target Tracking Using Neural Radiance Fields in Dense Urban Environments

IROS 2024poster

We study pursuit-evasion games in highly occluded urban environments, e.g. tall buildings in a city, where a scout (quadrotor) tracks multiple dynamic targets on the ground. We show that we can build a neural radiance field (NeRF) representation of the city—online—using RGB and depth images from dif…

Cited by 1SourcecodeScholar
2021

Scalable Reinforcement Learning Policies for Multi-Agent Control

IROS 2021poster

We develop a Multi-Agent Reinforcement Learning (MARL) method to learn scalable control policies for target tracking. Our method can handle an arbitrary number of pursuers and targets; we show results for tasks consisting up to 1000 pursuers tracking 1000 targets. We use a decentralized, partially-o…

Cited by 42SourcecodeScholar