Jae-cheol Sim

Seoul, South Korea

Samsung Electronics

Hello! I am Jae-cheol Sim, currently working as a Staff Engineer at the AX Development Group in Samsung Electronics.

I am deeply interested in LLMs, AI Transformation (AX), and Robotics. As part of my ongoing research and studies, I maintain an open repository that centralizes all LLM-related technologies. You can check it out here: 👉 All About LLM: A comprehensive guide and documentation on all LLM-related technologies.

Experience

Samsung Electronics (Seoul, South Korea) Total Duration: 2018 – Present

AX Engineer | AX Development Group Dec 2025 – Present
- Developing Enterprise AI Transformation (AX) solutions.
AI Research Engineer (Productivity & LLM) | AI Productivity Team / Language Intelligence Team, Samsung Research Jan 2024 – Dec 2025
- PoC to Production: Scaled internal productivity AI services from PoC (2024) to enterprise-wide launch (2025) using Samsung Gauss (Proprietary LLM).
- Full-Stack Development: Led end-to-end engineering (FE/BE) to ensure a seamless user experience for AI services.
- Advanced AI Design: Designed and implemented core AI components, focusing on RAG architectures and Autonomous Agents to enhance accuracy and automation.
AI Research Engineer (Simulation & Generative Methods) | AI Methods Team, Global AI Center Jan 2022 – Dec 2023
- OpenBotHandy Project Lead: Established standardized simulation benchmarks for robotic manipulation to accelerate community research.
- GenAI for Simulation: Engineered automated 3D environment generation pipelines converting sensor data and floor plans into interactive interiors.
- Virtual Benchmarking: Conducted high-fidelity simulations to validate next-gen robotic manipulation technologies.
Robotics Research Engineer (Simulation & RL) | Simulation Lab / Frontier Research Team, Robot Center Jan 2018 – Dec 2021
- RL & Sim2Real: Authored research on RL for vision-based manipulation (arXiv:2206.05671) and achieved Sim2Real transfer for physical robots (BotHandy).
- Synthetic Data Platform: Architected a web-based platform for procedural indoor generation and synthetic data management.
- Algorithm Validation: Developed and validated semantic object mapping algorithms for vacuum cleaners via simulation.

Education

Chung-Ang University (Seoul, South Korea) B.S. in Computer Science & Engineering Mar 2011 – Feb 2018

news

Apr 01, 2026	Promoted to Staff Engineer.
Nov 01, 2025	Transferred to AX Development Group (formerly Samsung Gauss Development Group), Samsung Electronics.

selected publications

Reinforcement Learning for Vision-based Object Manipulation with Non-parametric Policy and Action Primitives

Dongwon Son, Myungsin Kim, Jaecheol Sim, and 1 more author

arXiv preprint arXiv:2206.05671, 2022

Abs PDF Video

The object manipulation is a crucial ability for a service robot, but it is hard to solve with reinforcement learning due to some reasons such as sample efficiency. In this paper, to tackle this object manipulation, we propose a novel framework, AP-NPQL (Non-Parametric Q Learning with Action Primitives), that can efficiently solve the object manipulation with visual input and sparse reward, by utilizing a non-parametric policy for reinforcement learning and appropriate behavior prior for the object manipulation. We evaluate the efficiency and the performance of the proposed AP-NPQL for four object manipulation tasks on simulation (pushing plate, stacking box, flipping cup, and picking and placing plate), and it turns out that our AP-NPQL outperforms the state-of-the-art algorithms based on parametric policy and behavior prior in terms of learning time and task success rate. We also successfully transfer and validate the learned policy of the plate pick-and-place task to the real robot in a sim-to-real manner.