Research
NuRI Lab studies how robots learn reusable skills, construct predictive world models, navigate dynamic environments, and manipulate objects purposefully alongside people.
Research Topics
Robot Learning
We study how robots acquire reusable skills from demonstrations, interaction, and feedback. Our work combines imitation learning, reinforcement learning, language-conditioned policies, human-inspired learning, and safe adaptation in real-world environments.
World Models
We develop predictive representations that allow robots to understand how environments may change and anticipate the consequences of their actions. Our research connects spatial reasoning, future prediction, 3D scene representation, causal reasoning, and planning.
Robot Navigation
We build language-guided and socially aware navigation systems for dynamic environments. Our work combines visual navigation, semantic mapping, spatial memory, 3D Gaussian Splatting, and multimodal scene understanding.
Robot Manipulation
We study perception-driven grasping, object-centric 3D reconstruction, and long-horizon task execution. Our research integrates task and motion planning, skill chaining, reusable policies, and interaction for robust real-world manipulation.
Projects
[AI Star Fellowship] Research on AI Digital Performer Generation and Control Intelligence for Virtual Production
The project creates intelligent digital performers that understand immersive environments, respond to user needs, and become more helpful, natural, and reliable through continuous interaction and feedback.
Funded by the Ministry of Science and ICT (MSIT) through IITP.
Past Projects
[Physical AI PoC] Physical AI-based PoC Platform for Advanced-Manufacturing Technology Validation
A scalable verification platform integrating perception, reasoning, and action for data-driven, automated, and human-aligned manufacturing technology validation.
Funded by the Ministry of Science and ICT (MSIT) through NIPA · 2025.08–2025.12.
[Navi AI] Development of AI Technology for Guidance of a Mobile Robot to its Goal with Uncertain Maps in Indoor/Outdoor Environments
Machine-learning techniques that enable mobile robots to navigate public places reliably without requiring highly accurate maps to be maintained at all times.
Funded by the Ministry of Science and ICT (MSIT) through IITP · Research participation: 2019.04–2022.12.
[Brain AI] Brain-Inspired AI with Human-Like Intelligence
Research on developmental cognition, computational neuroscience, and brain-based artificial intelligence toward machines with incrementally growing cognitive abilities.
Funded by the Ministry of Science and ICT (MSIT) through IITP · Research participation: 2019.04–2022.12.
[SW Star Lab] Robot Learning: Efficient, Safe, and Socially-Acceptable Machine Learning
Research on data-efficient, safe, and socially acceptable robot learning for autonomous systems operating and interacting alongside people in dynamic environments.
Funded by the Ministry of Science and ICT (MSIT) through IITP · Research participation: 2019.04–2022.12.