Nuri Kim

Nuri Kim

Assistant Professor

Department of Electronics Engineering, Jeonbuk National University

At Neural Robot Intelligence Laboratory (NuRI Lab), we explore how robots can develop world models to make informed decisions, utilize visual navigation for goal-directed movement, and leverage 3D Gaussian Splatting for enhanced spatial perception. Our research integrates techniques from task and motion planning, semantic mapping, skill chaining, and multi-modal perception to build robots that can operate robustly in real-world environments and collaborate effectively with humans.

Education

Seoul National University

Ph.D. in Electrical & Computer Engineering

Mar 2016 - Feb 2023

Advisor: Prof. Songhwai Oh

Korea University

B.S. in Electrical Engineering

Mar 2012 - Feb 2016

Graduated with Highest Honor (GPA: 4.2/4.5)

News

Jul 2026

Selected for the MSIT AI Star Fellowship, a six-year program starting in 2026, to work on virtual space understanding, reinforcement learning-based navigation, and self-improving AI characters.

Jun 2026

Invited talk @ Gachon University.

Jun 2026

Co-organizing the SeMaNa: Semantic-Aware Mapping and Navigation workshop @ IROS 2026.

May 2026

Guest editor for the ICROS special issue on Physical AI for Robotic Autonomy in Manufacturing and Logistics.

Jan 2026

Invited talk @ KETI.

Dec 2025

Invited talk @ Seoul AI Foundation.

Mar 2025

Joined Jeonbuk National Univ. as an Assistant Professor in Electronics Engineering.

Sep 2024

SEA accepted to Knowledge-Based Systems.

Mar 2023

Start working @ SAIT autonomous car team.

Feb 2023

Invited talk @ KAIST.

Dec 2022

Successfully finished Ph.D. Thesis Defense.

Dec 2022

Oral presentation at CoRL 2022.

Sep 2022

TSGM accepted to CoRL 2022 as an oral presentation.

Publications

SEA Overview

Semantic Environment Atlas for Object-Goal Navigation

Nuri Kim, Jeongho Park, Mineui Hong, and Songhwai Oh

Knowledge-Based Systems, Volume 304, 25 November 2024, 112446

PDF arXiv
TSGM Overview

Topological Semantic Graph Memory for Image-Goal Navigation

Nuri Kim, Obin Kwon, Hwiyeon Yoo, Yunho Choi, Jeongho Park, and Songhwai Oh

Conference on Robot Learning (CoRL-22) Oral presentation

Project Page PDF arXiv Code Video
VGM Overview

Visual Graph Memory with Unsupervised Representation for Visual Navigation

Obin Kwon, Nuri Kim, Yunho Choi, Hwiyeon Yoo, Jeongho Park, and Songhwai Oh

International Conference on Computer Vision (ICCV-21)

Project Page PDF Code Video
IDNet Overview

Learning Instance-Aware Object Detection Using Determinantal Point Processes

Nuri Kim, Donghoon Lee, and Songhwai Oh

Computer Vision and Image Understanding (CVIU-20)

PDF Code
Text2Pickup Overview

Interactive Text2Pickup Networks for Natural Language-Based Human-Robot Collaboration

Hyemin Ahn, Sungjoon Choi, Nuri Kim, Geonho Cha, and Songhwai Oh

IEEE Robotics and Automation Letters (RAL-18) and IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-18)

PDF Video

Talks

ICML 2026 Robotics Research Trends

Lab Seminar, Jeonbuk National University, Jul 20, 2026

Slides

From Hard-Coded Robots to World Models: The Evolution of Physical AI

Invited Talk, Gachon University, Jun 26, 2026

Advanced Sensing Technologies for Physical AI

Invited Talk, KETI, Jan 2, 2026

What Are World Models? The Next Frontier of Physical AI

Invited Talk, Seoul AI Foundation, Dec 12, 2025

Understanding LLMs and the Future of AI

Invited Talk, Jeonbuk National University, May 2025

Semantic Visual Navigation for Embodied Agents: A Graph-Based Approach

Invited Talk, KAIST, Feb 2023

Slides

CoRL 2022 Review

Lab Seminar, Seoul National University, Jan 2023

Slides

Oral Presentation of TSGM

Conference on Robot Learning, Auckland, New Zealand, Dec 2022

Video Slides

Honors and Awards

Brain Korea 21 Plus Scholarship

Seoul National University

2019-2021

Great Paper Award

Korean Institute of Information Scientists and Engineers

2017

Lecture & Research Scholarship

Seoul National University

2016

Graduate with Great Honor

Korea University

2016

Creative Challenger Scholarship

Korea University

2015

National Scholarship For Science and Engineering

Korea Student Aid Foundation (KOSAF)

2014-2015

Teaching

Advanced Deep Learning

Graduate

Jeonbuk National University

Fall 2026

Deep Learning

Undergraduate

Jeonbuk National University

Fall 2026

Image Processing

Undergraduate

Jeonbuk National University

Spring 2026

Introduction to Robot Learning

Graduate

Jeonbuk National University

Fall 2025

Computer Science and Programming

Undergraduate

Jeonbuk National University

Fall 2025, Spring 2026

Professional Service

Workshop Organizer

Guest Editor

Associate Editor

  • International Conference on Ubiquitous Robots (UR) 2025

Conference and Journal Reviewing

  • ECCV 2026
  • CVPR 2026
  • TPAMI 2025
  • IROS 2024–2025
  • ICRA 2024
  • UR 2022–2025
  • T-RO 2020–2023
  • RA-L 2022

Projects

[AI Star Fellowship] Research on AI Digital Performer Generation and Control Intelligence for Virtual Production

2026.07. ~ 2031.12.

The goal of this project is to create intelligent digital performers that can naturally guide, accompany, and assist users in virtual production and immersive environments. These characters are designed to understand their surroundings, respond appropriately to user needs, and adapt their behavior over time. Ultimately, the project aims to develop digital performers that become more helpful, natural, and reliable through continuous interaction and feedback.

Funded by the Ministry of Science and ICT (MSIT).

Past Projects

[Physical AI PoC] Physical AI-based PoC Platform for Advanced-Manufacturing Technology Validation

2025.08. ~ 2025.12.

For effective validation of advanced manufacturing technologies, a scalable and adaptive verification framework is essential. However, validating new technologies in real production environments is both costly and operationally risky. In this project, we proposed a Physical AI-based PoC platform that integrates perception, reasoning, and action to support data-driven and automated manufacturing technology validation. The ultimate goal of the project was to build a next-generation Physical AI foundation model that enables reliable, intelligent, and human-aligned manufacturing technology verification.

  • Proposed a Physical AI-based PoC platform that integrates perception, reasoning, and action to support data-driven and automated manufacturing technology validation.
  • Built a next-generation Physical AI foundation model that enables reliable, intelligent, and human-aligned manufacturing technology verification.

Funded by the Ministry of Science and ICT (MSIT).

[Navi AI] AI Technology for Guidance of Mobile Robots with Uncertain Maps

2019.01. ~ 2023.12.

For reliable navigation in public places, a highly accurate map is required for a mobile robot. However, it is extremely time-consuming and expensive to maintain accurate maps of all places at all times. In this project, we developed a new class of machine learning techniques to overcome this challenge in order for a mobile robot to reliably navigate public places without the need for highly accurate maps. The ultimate goal of the project was to develop human-like navigation skills for mobile robots.

  • Developed an indoor environment navigation robot that works even in unknown environments by leveraging semantic understanding when maps are unavailable.

Funded by the Ministry of Science and ICT (MSIT).

[Brain AI] Brain-Inspired AI with Human-Like Intelligence

2019.01. ~ 2023.12.

The goal of this project was to understand the progressive developmental process of the basic principles of intelligence and cognitive abilities of the human brain using developmental cognitive theory, computational neuroscience, and brain-based artificial intelligence. In addition, we aimed to develop the next-generation machine learning technology which can simulate a brain with child-level cognitive abilities through incremental growth.

  • Developed a reliable object detector in occluded environments.

Funded by the Ministry of Science and ICT (MSIT).

[SW Star Lab] Robot Learning: Efficient, Safe, and Socially-Acceptable Machine Learning

2019.01. ~ 2023.12.

The goal of this project was to develop efficient, safe, and socially friendly machine learning so that autonomous robots can coexist with people in various environments. In this project, we developed socially friendly robot learning technology that enables efficient reinforcement learning with fewer data and ensures safety. The developed technology was applied to autonomous robots for verification and further refinement. The main applications of this project were a delivery robot based on an autonomous driving algorithm and a safe and socially friendly housekeeping robot using image and language information. In addition, we developed core technologies for autonomous robots and shared the developed software with the AI and robotics communities.

  • Developed a robot navigation technology capable of predicting crowd trajectories and performing social actions in various crowd cluster scenarios.

Funded by the Ministry of Science and ICT (MSIT).

Personal Projects

Quantitative Trading

2020. ~ Present

Development of algorithms for finding an optimal portfolio ratio.

Nurihobot

2023.09. ~ 2025.02. @ Samsung Advanced Institute of Technology (SAIT)

An AI assistant bot on the Knox messenger, built as an AX (AI transformation) tool for employees. It serves an open-source LLM for multi-turn conversation, and uses retrieval-augmented generation (RAG) over Confluence data to track the status of ongoing work and send notifications.

Where to Meet

2017

A web app built to solve the hassle of deciding where a group of friends should meet, finding a spot that works for everyone.