Papers
7
Total Citations
58
H-Index
4
About
Li Cai’s research lies at the intersection of robotics, artificial intelligence, and human-robot interaction, with a focus on enabling machines to learn, move, and perceive like living beings. Her most influential work, “Humanoids Learning to Walk: A Natural CPG-Actor-Critic Architecture” (2013, 32 citations), introduced a novel framework combining Central Pattern Generators (CPGs) with reinforcement learning to teach humanoid robots locomotion through dynamic environmental interaction—a breakthrough that addresses long-standing challenges in adaptive robotic gait. She further advanced this line of inquiry by modeling infant crawling postures on the NAO robot, demonstrating how bio-inspired neural networks can learn proper motor skills. Cai has also pioneered the classification of social gestures and grasping actions using kinematic data and machine learning, enabling robots to interpret human affective motion for more natural collaboration. More recently, she has applied her expertise to real-world automation, developing complete coverage path planning algorithms for search-and-rescue robots using Artificial Bee Colony optimization, and creating autonomous inspection technologies for substations that integrate image recognition and computer vision. Her work on UAV-based electromagnetic safety in substations underscores her commitment to practical, high-impact solutions. With a growing citation record spanning foundational locomotion studies to applied robotics, Cai is shaping the future of intelligent, socially aware, and industrially capable machines.
Research Focus
Key Achievements
Top Papers
- 1Humanoids Learning to Walk: A Natural CPG-Actor-Critic Architecture32 citations · 2013
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- 6Research on Electromagnetic Safety of UAV Inspection in Substation2 citations · 2025
- 7Research on robot path planning based on machine vision1 citations · 2022