Khoa Vo

Gripper-aware models can dramatically improve robotic manipulation by tailoring strategies to specific gripper types, leading to better performance in diverse tasks.

Advanced Vision-Language-Action models can be dramatically compressed by up to 50% without losing performance, reshaping our approach to robotic manipulation.

Separating appearance from identity in video object learning leads to significant gains in segmentation and temporal consistency, outperforming existing methods.

Rare but critical categories like children and emergency vehicles can be effectively detected using a novel semantic-guided expert framework that transforms long-tail learning in 3D perception.