Grace Zhang

Cornell University, Southern California University for Professional Studies

Papers

Total Citations

H-Index

About

Grace Zhang is a pioneering roboticist at the intersection of soft robotics and human-robot interaction, whose work is redefining how machines physically engage with the world. Her primary research areas include tactile sensing, policy transfer for robot learning, and safe physical human-robot interaction (pHRI). Zhang’s most notable contribution is the development of **CushSense**, a fabric-based, soft, and stretchable tactile-sensing skin designed to provide whole-arm feedback for robots. This innovation is critical for applications like robotic caregiving, where safe and comfortable contact is paramount. Her work on CushSense has already garnered 9 citations, signaling its immediate impact on the field of soft haptics. Additionally, Zhang has made significant strides in robot learning through her research on **policy transfer across visual and dynamics domain gaps**. Her 2021 paper on iterative grounding (5 citations) addresses the challenge of transferring policies from simulation to real-world environments without task supervision—a key bottleneck in efficient robot learning. By bridging these gaps, Zhang is enabling more robust and adaptable robotic systems. Her achievements mark her as a rising leader in creating robots that are not only safer but also more intelligent in their interactions.