Jiayuan Mao

Neuro-Symbolic Planning Makes Robot Learning More Data-Efficient

Jiayuan Mao, a Member of Technical Staff at Amazon Frontier AI & Robotics and incoming University of Pennsylvania assistant professor, argues in a Stanford Robotics Seminar that robot learning should be built around planning over compositional world models rather than direct policy fitting alone. His case is that neuro-symbolic systems — neural models embedded in symbolic constraint graphs for objects, relations, actions and effects — can learn from few demonstrations, compose skills at inference time and generalize to new objects, states and goals more reliably than end-to-end policies.