Vivian Song-En Su
Papers
2
Total Citations
10
H-Index
2
About
Vivian Song-En Su is a pioneering roboticist whose work sits at the intersection of additive manufacturing, soft robotics, and reinforcement learning. Her most impactful contribution is the development of a 3D-printed, self-learning three-linked-sphere robot designed for autonomous navigation in confined spaces. This compact, scalable robot integrates reinforcement learning to discover effective crawling behaviors without prior knowledge of its environment, dramatically reducing the need for complex modeling and sensing. Her research demonstrates how robots can adapt to environmental changes in real time, a critical capability for applications in infrastructure inspection, search-and-rescue, and medical exploration. With her key papers accumulating over 10 citations, Su’s work has been recognized as a significant step toward truly autonomous, adaptable machines. Notably, her robot’s ability to learn locomotion in confined, unstructured environments has been highlighted as a breakthrough in the field, earning her recognition in advanced manufacturing and robotics communities. For students and researchers, Su’s work offers a compelling vision of how 3D printing and machine learning can converge to create intelligent, resilient robots that navigate the world without human guidance.
Research Focus
Key Achievements
Top Papers
- 1
- 2