Cognitive Learning for Vision and Robotics (CLVR) Lab
Led by Joseph J. Lim at KAIST, CLVR develops intelligent robotic systems that make sequential decisions through perception, action, and reasoning. The lab focuses on reinforcement learning, world models with representation learning, visual perception, and symbolic manipulation.
Notable achievements
Research in embodied AI and robot learning; development of world models for robotics
Notable work
Recent publications
All papers →Matched by this lab's specialties (keyword overlap + direct affiliation)
PAEAR: Point Clouds Area Exploration and Active Recognition method driven by reinforcement learning for robotic welding
Yong Tao, Donghua Tan, Fan Ren +6 more
Robotics and Computer-Integrated Manufacturing · 2026
Multi-pass cutting parameters optimisation with causal reinforcement learning for deformation control of thin-walled parts
Fengyi Lu, Guanghui Zhou, Chao Zhang +2 more
Robotics and Computer-Integrated Manufacturing · 2026
MA2MB: Multi-agent mutual-advising model-based reinforcement learning for pursuit and evasion games
Baolin Zhao, Qi Guo, Xiandong Wang +2 more
Robotics and Autonomous Systems · 2026
Container Unloading via Reinforcement Learning: Picking Order, Deadlock Avoidance, and Proof-of-Concept Simulation
Jan Rüdiger, Max Schenke, Daniel Weber
2026
Learning to Balance Motor Thermal Safety and Quadrupedal Locomotion Performance with Residual Policy
Yuhang Wan, Weixian Lin, Letian Qian +5 more
2026
Slot-MPC: Goal-Conditioned Model Predictive Control with Object-Centric Representations
Jonathan Spieler, Angel Villar-Corrales, Sven Behnke
2026