Boseong Jeon

🤖 I deliver a full-stack robot autonomy.


1. Education

2. Core Research

A. Hierarchical Motion Planning for Active Vision

Designed a real-time motion planning framework for generating high-quality robot trajectories that balance multiple complex and often conflicting objectives. Specifically, I developed an aerial chasing system that maintains target visibility, ensures safety, and optimizes travel efficiency simultaneously. All algorithms were deployed on onboard computers, and I independently built the entire system, including the hardware. Watch the demo on Youtube (opens in a new tab) 🚀

Academic Publications (1st Author Only)

B. On-device generative AI (Galaxy AI)

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Led the development of an on-device generative image model deployed on Galaxy S25, delivering performance comparable to Google’s Imagen 3 cloud model. I was responsible for the entire training and data pipeline, including: 1) LoRA-based adaptation of a large foundation model, 2) Downstream fine-tuning for image generation tasks, and 3) Reward modeling and fine-tuning prior to quantization for efficient on-device deployment. Special focus on enabling a single model to perform multiple tasks and enhance output quality, despite severe constraints on memory and computation time.

Technical Reports

I wrote relevant articles. Please note that our company does not allocate dedicated time for research or paper writing.

C. Robot Foundation Model (A+B)

Will be updated soon 🔥

3. Past Projects

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Task Allocation for Rescue Robots (2017~2019)

Performed research with KIRO. Given a fleet of atypical robots, assign the task sequence to each robot considering their mobility and mapping information. Developed auction algorithm incorporating A* optimal connecting path.

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Autonomous Driving in Unstructured Environments (2019~2020)

Developed a driving stack for unstructured roads using sensors such as LiDAR and GPS. With KETI, we conducted real-car testing. The stack includes ground-based mapping, moving object prediction, and hierarchical motion planning composed of safe driving area estimation and an MPC reference tracker. YouTube

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traj_gen: A Continuous Trajectory Generation with Simple API (230+ Stars)

traj_gen is a continuous trajectory generation package where high-order derivatives along the trajectory are minimized while satisfying waypoints (equality) and axis-parallel box constraints (inequality). The objective and constraints are formulated in quadratic programming (QP) to ensure real-time performance. GitHub

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Incubation Project for Advancing Navigation of Samsung Robot Platform (SRP) (2023)

As part of an initiative to extend the navigation tree of SRP with forward–backward motion and rectangular collision modeling, I developed a reference pose trajectory and its corresponding safe corridor using a variant of the hybrid A* algorithm (YouTube).
A real-robot demonstration video is available here.