Chenjian Song
Papers
4
Total Citations
27
H-Index
3
About
Chenjian Song is a robotics researcher whose work focuses on physical human-robot interaction (pHRI), path learning, and compliant control. His major contributions lie in developing methods that allow robots to learn and adapt to human-guided tasks through iterative interaction. Notably, his most cited work, "Waypoints updating based on Adam and ILC for path learning in physical human-robot interaction" (2021, 10 citations), introduces a novel approach combining the Adam optimization algorithm with iterative learning control (ILC) to generate and update reference waypoints for path tracking. In "Path Recognition and Virtual Guides Design for Path Following Based on Human–Robot Collaboration" (2022, 8 citations), he employs support vector machines to recognize desired paths and design virtual guides for collaborative tasks. His paper "Path Learning by Demonstration for Iterative Human–Robot Interaction With Uncertain Time Durations" (2022, 7 citations) further advances pHRI by using stretch-compression ILC and contouring impedance control to learn task paths despite variable time durations. Song’s work on adaptive virtual guides for compliance control skill teaching (2022, 2 citations) addresses the challenge of reducing cognitive and physical loads on human operators during demonstrations. Collectively, his research enhances robot adaptability and safety in collaborative environments, with a growing citation impact that underscores its relevance to modern robotics.
Research Focus
Key Achievements
Top Papers
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