Max Robotics — Global Service Robot Directory + US Certification

About

Chung Min Kim is a leading researcher at the intersection of robotics, computer vision, and natural language processing, with a focus on enabling robots to understand and interact with the physical world through language. His most impactful contribution is **Language Embedded Radiance Fields (LERF)** (2023, 291 citations), a groundbreaking method that grounds natural language descriptions directly into 3D neural radiance fields, allowing robots to query specific locations based on visual appearance, semantics, or abstract associations. This work has become a foundational tool for semantic 3D mapping and object search. Building on this, Kim developed **Language-Embedded Gaussian Splats (LEGS)** (2024) and **Lifelong LERF** (2024), which enable mobile robots to incrementally build and maintain room-scale semantic maps for long-term inventory monitoring. In manipulation, his **IPC-GraspSim** (2022) reduces the Sim2Real gap for parallel-jaw grasping by accurately modeling soft compliant jaw tips, while his **“Bluction” tool** (2022) and mechanical search algorithms tackle the challenging problem of retrieving objects from cluttered shelves. With over 340 citations across his portfolio, Kim’s work is widely recognized for bridging language understanding and physical robot interaction.