Ce Cao

Papers

Total Citations

H-Index

About

Ce Cao is a robotics researcher whose work focuses on advancing robot motor skill acquisition through innovative learning frameworks. Her primary research areas include learning from demonstration (LfD) and reinforcement learning, with a particular emphasis on bridging the gap between human demonstration and autonomous robot execution. Cao's major contribution lies in developing a novel "alternate learning in two spaces" approach, which enables robots to more effectively transfer and generalize motor skills across similar tasks. This method addresses a critical challenge in robotics: automatically generating new motions for novel tasks without starting from scratch. Her 2020 paper on this topic has garnered 11 citations, reflecting growing interest in her work within the robotics community. Cao's research has significant implications for industrial automation and assistive robotics, where robots must adapt learned skills to new environments. Her earlier 2019 publication laid the foundational concepts for this dual-space learning paradigm. As a rising researcher in the field, Cao's work represents an important step toward more flexible and intelligent robotic systems capable of learning from human demonstrations and improving through reinforcement learning.