Minsik Jeon

I am a MS in Robotics student at Carnegie Mellon University (CMU). My research interests lie in computer vision and robotics, with a special focus on their robustness and generalizability.
Prior to joining CMU, I served as a researcher at the Agency for Defense Development (ADD) in the South Korea while concurrently completing my mandatory military service. I completed my bachelor's degree (Summa Cum Laude) in School of Computing at KAIST (Korea Advanced Institute of Science and Technology).

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Research Interests

I am interested in the intersection of robotics and computer vision, with the goal of developing generalizable robotic perception system that adapts through feedback gathered during operation, enhancing scalability and reliability in unfamiliar environments over long-term deployments. I am particularly interested in methods to adapt perception models to novel environments and reduce uncertainties, mitigate ambiguity through multimodal integration, and integrate perception with navigation for active data acquisition.
My current & future interests are as follows:

● Robotic Perception
● Uncertainty-aware Perception & Navigation
● Active Perception
● Domain Adaptation & Generalization
● Self-supervised Learning
● Sensor Fusion

Education

Publications

A method for Open-World object detection that improves novel object detection and semantically rich feature extraction by leveraging Vision Foundation Models.

An UDA framework for object detection that can effectively adapt to real-world adverse weather conditions by addressing Style & Weather gaps separately.

Projects

- Developing perception and control algorithms for off-road navigation: Robust moving object detection & tracking in off-road environment, semantic terrain classification map through LiDAR-Camera integration.
- Examined path planning and control algorithms for navigation in complex environments.

- Developed an LiDAR-IR fusion method for robust object detection under visibility-constrainted scenarios.

- Developed an open-set 2D & 3D object detection method for LiDAR pointcloud.

Participated in the Indy Autonomous Challenge (IAC), the first autonomous car racing competition to be held in Indianapolis, as an intern for Team KAIST. As part of a perception team, I tested algorithms using camera and lidar in a simulation environment.

Developed a removal network that addresses the line artifact of SCAPE microscopy image, using CycleGAN-like model and transferring the image into a CFM style.

In Performance analysis team, configured On-board RAID using SSDs and conducted performance evaluations for each RAID option.

Template from Jon Barron's wonderful work.