Jun Ma

Multi-UAV exploration gets a 43% speed boost with C$^2$-Explorer, a decentralized framework that smartly carves up environments and assigns tasks for contiguous, efficient traversal.

Achieve sharper, more accurate infrared super-resolution in real-world conditions by disentangling thermal and structural degradations with a novel autoregressive framework.

Humanoid robots can now nimbly manipulate objects across much larger workspaces thanks to a LiDAR-powered perception system that eliminates the need for constant repositioning.

Safe, collaborative transport of payloads by quadruped robots in tight spaces is now possible thanks to a novel reinforcement learning approach that guarantees collision avoidance.

Achieve 44-48% higher accuracy in UAV landing on oscillating marine platforms by explicitly modeling wave harmonics and fusing frequency-domain and time-domain prediction.