Exclusive | Peking University's Dong Hao: "Scaling Laws That Only Stay at the Data Level Cannot Teach General Robots"
Dong Hao, a vice professor at Peking University and chief scientist at Shangwei Qiyuan, proposes a new paradigm for embodied AI development. He argues that current methods relying solely on imitation learning or reinforcement learning have limitations, particularly in handling errors and achieving general intelligence. Hao advocates for a two-dimensional "Scaling Law" that considers both the quantity of data and the number of tasks, aiming for robots that become more efficient and capable with more learning. AI
IMPACT This new 2D Scaling Law could accelerate the development of general-purpose robots by making learning more efficient.