AI & ROBOTICS LABORATORY

The laboratory (formerly known as Vision & Learning Lab) was formed in Mar. 2020.

The AI & Robotics Laboratory at CAU aims to push the boundary of robot learning research by developing efficient, versatile, and scalable models, towards general-purpose AI. Our research explores methods that enables robots to perceive, reason about, and learn diverse real-world tasks through interaction with the physical world.

Our research interests include efficient machine learning, multi-modal learning, multi-task learning, continual learning, machine unlearning, and their applications to robotics and computer vision, but not limited to.

NEWS (More news…)

• [02/2026] Three papers are accepted to CVPR 2026 (2 Main, 1 Findings).
• [01/2026] A paper on cross-expanding incremental learning is accepted to ICLR 2026.
• [09/2025] A paper on in-context learning agents is accepted to CoRL 2025 Workshop.
• [09/2025] A paper on cross-modal learning is accepted to Neural Networks.
• [06/2025] Two papers on continual learning are accepted to ICCV 2025.
• [05/2025] A paper on 3D human pose estimation is accepted to CVIU.
• [03/2025] A paper on efficient continual learning is accepted to Neural Networks.
• [02/2025] A paper on multi-modal learning is accepted to CVPR 2025 (Highlight).
• [01/2025] A paper on self-corrective robot task planning is accepted to ICRA 2025.
• [10/2024] A paper on continual learning is accepted to NeurIPS 2024 Workshop.
• [06/2024] A paper on task planning based on LLMs is accepted to IROS 2024.
• [04/2024] A paper on neural architecture search is accepted to IEEE TIP.