Vision–Force Multimodal Imitation Learning Force-feedback Leader-arm Teleoperation · Impedance Control

Vision–force learning for dexterous bimanual control.

I develop learning-based systems for precise dual-arm and dual-hand manipulation from multimodal demonstrations.

Portrait of Chemin Ahn
M.S. Researcher
Robotics Innovatory, SKKU
2026 Highlight 1st Prize KRoC RED Show
Current Focus Multimodal Imitation Learning Vision + Force

Patent application
Force-feedback leader arm

Robotics Conference Award
1st Prize · KRoC 2026 RED Show

Robot Control Internship
Doosan Robotics · Jul - Oct 2024

Final M.S. GPA
4.0 / 4.5 · SKKU

01 Profile

Researching the bridge between human intent and robot action.

I am a Mechanical Engineering M.S. student at Sungkyunkwan University specializing in robotics. My work focuses on learning and control systems for manipulators that can acquire precise, transferable skills from human demonstrations.

I have built a VR-tracker teleoperation system and, separately, a force-feedback leader-arm teleoperation system designed specifically to collect high-quality demonstration data. I also developed a ROS 2 visual-servoing package during an internship with the Robot Control Team at Doosan Robotics.

For imitation learning, I have evaluated multiple policies and modified their internal architectures to improve learning performance. My current research extends vision–proprioception policy architectures to learn from force as an additional modality for dexterous, learning-based control of dual-arm robots with hands.

01

Imitation Learning

Exploring multiple policies, refining their internal architectures, and extending vision–proprioception learning with force for dexterous control.

02

Teleoperation

VR-tracker teleoperation and, separately, force-feedback leader-arm teleoperation for high-quality demonstration collection.

03

Robot Control

Impedance control, visual servoing, and gravity compensation.

02 Projects

Robotics systems built from research to real hardware.

Illustration of a dual-arm robot learning a manipulation task Robot Learning
02Research Project

Imitation Learning for Dexterous Manipulation

Evaluated and customized multiple policy architectures, collected teleoperation demonstrations, and deployed policies across single-arm, dual-arm, and dual-arm-with-hands setups. Current work extends vision–proprioception inputs with force.

PythonPyTorchImitation LearningROS 1/2
Illustration representing a visual-servoing robot system Robot Control
03Doosan Robotics

ROS 2 Visual Servoing

Developed a Python-based ROS 2 visual-servoing package, built an SDF/Gazebo simulation, and ran the system in simulation and on physical hardware.

PythonROS 2GazeboSDFDoosan Robotics
Illustration of VR-tracker teleoperation with a dual-arm robot Teleoperation
04Research Project

VR-Tracker Teleoperation

Built a VR-tracker teleoperation system for demonstration collection with transformation-matrix-based safety limits and a SLAM-enabled variant.

PythonROS 1/2VR TrackersSLAM
Autonomous medication preparation and delivery robot system Integrated Robotics
05KG-KAIROS · 2nd Prize

Medication Preparation & Delivery

Designed a manipulator-and-AMR workflow that verifies patient information, prepares prescribed medication, and delivers it autonomously.

PythonC++ROS 1StreamlitArduino
Custom autonomous mobile robot platform Autonomous Systems
06Capstone · 2nd Prize

Custom AMR: SLAM & Planner Comparison

Built a custom AMR and compared four navigation configurations combining GMapping and Hector SLAM with TEB and DWA local planners.

  • Raspberry Pi, Arduino Mega, encoder motors, and 2D LIDAR
  • ROS Navigation Stack integration and performance comparison
PythonC++ROS 1SLAM2D LIDAR

03 Experience

Research translated into industrial robotics.

2024

Jul — Oct
Seongnam, Korea

Industry Internship

Robot Control Team Intern

Doosan Robotics

Developed a visual-servoing example package for Doosan robot systems.

  • Implemented ROS 2 nodes in Python
  • Built an SDF/Gazebo simulation environment
  • Validated control in simulation and on physical hardware
View package on GitHub

04 Recognition

Work recognized through awards and intellectual property.

Intellectual Property Application filed

Force-Feedback Leader–Follower Teleoperation System for Contact-Rich Robot Manipulation

Inventor · System design · Force-feedback control · Teleoperation implementation

  • Provides operator force feedback during contact-rich manipulation.
  • Designed to improve demonstration quality for force-aware imitation learning.
Technical details remain undisclosed before patent publication.
2nd

2024 · Chung-Ang University LINC 3.0

Capstone Design Contest

Sensor and navigation-algorithm analysis for autonomous mobile robots

Awarded in June 2024, the study compared sensor options with navigation-algorithm characteristics for autonomous mobile robot design.

2nd

2024 · KG ICT

KG-KAIROS Youth AI Robotics Program

Pharmaceutical preparation and autonomous delivery using a cobot and AMR

Awarded in June 2024, the system paired a cobot for pharmaceutical preparation with an AMR for autonomous delivery.

05 Technical Toolkit

My hands-on toolkit for robotics research and development.

01

Programming

PythonC++MATLAB
02

Robotics & Simulation

ROS 1ROS 2GazeboIsaac SimIsaac Lab
03

Learning & Vision

PyTorchTensorFlowOpenCV
ACTDiffusion PolicyDiT PolicyACP
04

Scientific Computing

NumPyPandasMatplotlib
05

Robot Platforms

Doosan RoboticsRainbow RoboticsUniversal Robots
06

Engineering Tools

GitSOLIDWORKSSDFDynamixelwandb

Additional Republic of Korea Army · Sergeant · May 2021 — Nov 2022

06 Education

Mechanical engineering,
focused on
intelligent robotics.

Master of Science Ongoing

Sungkyunkwan
University

Suwon, Republic of Korea

MajorMechanical Engineering

GPA4.0 / 4.5

LabRobotics Innovatory

Selected Coursework
  • Intelligent Robotics
  • Robot Reinforcement Learning
  • Modern Control Systems
  • Human–Robot Collaboration
Bachelor of Science Cum Laude

Chung-Ang
University

Seoul, Republic of Korea

MajorMechanical Engineering

GPA3.84 / 4.5

SchoolMechanical Engineering

Selected Coursework
  • Robotics Engineering
  • Autonomous Control
  • Mechatronics
  • System Analysis
  • Dynamics
  • Visual Programming

07 Contact

Let’s build robots that learn from people.

Interested in robotics research, engineering opportunities, or collaboration?

chemx3937@gmail.com