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About

Qian Du’s research focuses on cognitive robotics, imitation learning, and human-robot interaction, with a particular emphasis on enabling robots to learn complex behaviors from human demonstration. Her most influential work, a 2011 paper with 30 citations, extends the Dynamic Movement Primitive (DMP) algorithm by integrating a potential field method for obstacle avoidance, allowing robots to adapt learned motions in dynamic environments—a critical step toward safe, autonomous operation. In her 2012 work on robot writing (12 citations), Du introduced a novel framework that combines motion learning with semantic knowledge acquisition, using a modified ISOMAP algorithm to map high-dimensional joint angles into low-dimensional trajectories, enabling robots to learn the motor skills and meaning behind writing numbers. Her broader framework for cognitive robots (5 citations) proposes a codebook-based method for object segmentation and recognition, coupled with speech interaction, allowing robots to learn task-related semantics through imitation and natural communication. Together, these contributions advance the field of developmental robotics, bridging low-level motor control with high-level cognitive understanding.