I'm a third-year PhD student in the Department of Computer Science at Stanford University. My research focuses on developing generalizable, data-efficient, and deployable methods for robot policy learning. I am advised by Jeannette Bohg as part of the Interactive Perception and Robot Learning Lab.
Previously, I received my Master's degree in Machine Learning at CMU, where I was co-advised by Katerina Fragkiadaki and Christopher G. Atkeson. Before that, I was an undergraduate student at USC, where I worked with Joseph Lim.
Mobi-π: Mobilizing Your Robot Learning Policy
Jingyun Yang, Isabella Huang*, Brandon Vu*, Max Bajracharya, Rika Antonova, Jeannette Bohg
Preprint
CUPID: Curating Data your Robot Loves with Influence Functions
Christopher Agia, Rohan Sinha, Jingyun Yang, Rika Antonova, Marco Pavone, Haruki Nishimura, Masha Itkina, Jeannette Bohg
Preprint
RSS 2025 Workshop on Robot Evaluation for the Real World, Best Paper Award
EquiBot: SIM(3)-Equivariant Diffusion Policy for Generalizable and Data Efficient Learning
Jingyun Yang*, Zi-ang Cao*, Congyue Deng, Rika Antonova, Shuran Song, Jeannette Bohg
CoRL 2024
CoRL 2024 Workshop on Whole-body Control and Bimanual Manipulation, Spotlight Presentation
Rethinking Optimization with Differentiable Simulation from a Global Perspective
Rika Antonova*, Jingyun Yang*, Krishna Murthy Jatavallabhula, Jeannette Bohg
CoRL 2022, Oral (6.5% acceptance rate)
Learning Periodic Tasks from Human Demonstrations
Jingyun Yang, Junwu Zhang, Connor Settle, Akshara Rai, Rika Antonova, Jeannette Bohg
ICRA 2022