Yi Zhong

Pairwise learning from driving data can significantly enhance proactive collision risk assessment, outperforming conventional methods that rely on limited numerical labels.

Achieving a 23.9% improvement in affordance segmentation accuracy, this model enables robots to seamlessly adapt to diverse real-world manipulation tasks without additional training.