Jiahe Wang

From the individual to humanity

and from humanity to nature

Struggle, conviction, and resilience—

all shaped by a quiet heart and a burning soul.

My research focuses on data-driven modeling and control of robotic systems, with particular interest in:

Soft robotics and compliant actuation
Koopman-based modeling and Model Predictive Control (MPC)
Embedded systems and multi-motor control
ROS 2-based sensing and control pipelines

I work on bridging theoretical modeling and real-world robotic implementation, developing systems that integrate sensing, actuation, and control for complex robotic platforms.

My recent work includes:

ROS2-based multi-motor control systems with real-time feedback
Camera-based tracking and data collection pipelines
Koopman operator learning for soft robot dynamics
Hardware integration using ESP32/Arduino and sensor systems

I am particularly interested in building scalable and robust robotic systems that combine physics-based understanding with data-driven methods.

📌 Feel free to explore my publications, projects, and blogs, or reach out for collaboration.

News

Apr 13, 2026	A bit surreal to see myself at the ASU homepage
Apr 01, 2026	Research featured in ASU News
Mar 03, 2026	First paper published in IEEE RA-L
Aug 14, 2024	I arrived in the United States to begin my PhD studies in Robotics at Arizona State University.

Latest Posts

Mar 28, 2026	Notes on Koopman operator learning
May 22, 2023	Research Visit to Tokyo Institute of Technology

Selected Publications

Data-Efficient Real-Time Control of an Artificial-Muscle-Driven Continuum Robot With Physics-Informed Koopman Operator

Jiahe Wang, Eron Ristich, Eric Weissman, and 2 more authors

IEEE Robotics and Automation Letters, 2026

DOI Bib Video

@article{11417449,
  author = {Wang, Jiahe and Ristich, Eron and Weissman, Eric and Ren, Yi and Sun, Jiefeng},
  journal = {IEEE Robotics and Automation Letters},
  title = {Data-Efficient Real-Time Control of an Artificial-Muscle-Driven Continuum Robot With Physics-Informed Koopman Operator},
  year = {2026},
  volume = {11},
  number = {4},
  pages = {5080--5087},
  keywords = {Data models;Continuum robots;Computational modeling;Training;Robots;Accuracy;Real-time systems;Overfitting;Nonlinear dynamical systems;Delays;Continuum robot;Koopman operator;physics-informed learning;data-driven control},
  doi = {10.1109/LRA.2026.3668444},
}