Our research spans the full spectrum of autonomous robotics, from physical platforms to AI foundation models.
Division 1: Adaptive Robot Platform
Research Leader: Prof. Ji-Hwan Yoo (KAIST)
Co-Leader: Prof. Kee-Wook Kyung (KAIST)
We develop physical platforms that understand and adapt to real-world interactions through advanced sensing, actuation, and control systems.
Unstructured Environment Response Platform
- Adaptive Actuators — Instantaneous response mechanisms for unpredictable environments
- Multi-modal Tactile Sensors — High-resolution sensing for material, force, and slip detection
- Intelligence-Embedded Modules — Distributed processing for rapid response
- Multi-Robot Platforms — Coordinated systems for complex tasks
Human-Level Precision Interaction Interface
- Dexterous Manipulation — Human-hand-level sensing and multi-DOF interfaces
- Haptic Feedback Systems — Real-time force and tactile feedback
- HRI-Based Collaborative Manipulation — Human-robot collaboration technologies
Real-Time Reflex Control System
- Material Deformation Detection — Instant sensing of physical changes
- Local Reflex Loops — Distributed control for immediate response
- Distributed Self-Intelligence — Autonomous decision-making at each module
Division 2: 5D Robot AI
Research Leader: Prof. Gyu-Bin Lee (GIST)
Co-Leader: Prof. Sung-Eui Yoon (KAIST)
We create AI foundation models that enable robots to understand and predict physical interactions in complex real-world scenarios.
5D Scene Graph World Foundation Model
- Autonomous Exploration — Self-directed data generation for 5D scene graphs
- 5D World Foundation Model — Comprehensive understanding of object states and interactions
- Physics-Informed Neural Networks (PINN) — Physics-embedded neural architectures
- Multi-Robot Sensor Fusion — Heterogeneous data integration for ultra-realistic modeling
Whole-Body Locomotion and Manipulation Foundation Model
- Multi-Modal Demonstration Learning — Training from diverse environment data
- Integrated Locomotion-Manipulation Control — Unified whole-body control systems
- Cross-Robot Transfer — Adaptation to various robot platforms
Real-Time Robot Response Foundation Model
- Multi-Modal Sensor Fusion — 5D world model learning systems
- Situation Prediction Models — Anticipating environmental changes
- Dynamic Object Avoidance — Real-time navigation in changing environments
- Social Autonomous Navigation — Human-aware locomotion systems