At the EAR Lab, we drive the evolution of Embodied AI from principle to practice. Our research centers on creating resilient perception systems and intelligent control algorithms that empower real-world robots. To achieve this, we actively solve foundational challenges across data-centric learning, robust multi-modal sensing, and autonomous navigation in complex environments.
Exploring how data-centric approaches can advance embodied AI systems, focusing on efficient learning and adaptation in real-world scenarios.
Developing algorithms that allow agents to learn policies from multiple sensor modalities.
Investigating real-world applications of embodied AI in areas such as autonomous navigation, manipulation, and multi-robot collaboration.