Yongxin Chen
Papers
8
Total Citations
150
H-Index
5
About
Yongxin Chen is a rising star at the intersection of control theory, optimal transport, and robotics, whose work bridges foundational mathematics with cutting-edge AI. His research centers on stochastic control, Schrödinger bridges, and diffusion models—themes that unify his contributions to motion planning, reinforcement learning, and robot manipulation. Chen’s most influential work, “Stochastic Control Liaisons: Richard Sinkhorn Meets Gaspard Monge on a Schrödinger Bridge” (2021, 102 citations), established a powerful connection between optimal transport and stochastic control, offering a unified framework that has become a cornerstone for modern inference and planning algorithms. He has since advanced practical robotics with innovations like “ReorientDiff” (2024, 13 citations), a diffusion model for object reorientation, and “Generative Skill Chaining” (2023), which tackles long-horizon manipulation planning. Chen also introduced “Sample-based Distributional Policy Gradient” (2020), extending distributional reinforcement learning to continuous control. His work on Gaussian variational inference for motion planning (2023) and particle filtering on Lie groups (2022) further demonstrates his ability to solve complex, real-world problems. With a growing citation record and a knack for translating deep theory into deployable algorithms, Chen is shaping the future of intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2A Gaussian Variational Inference Approach to Motion Planning13 citations · 2023
- 3ReorientDiff: Diffusion Model based Reorientation for Object Manipulation13 citations · 2024
- 4Sample-based Distributional Policy Gradient7 citations · 2020
- 5
- 6
- 7An Optimal Control Approach to Particle Filtering on Lie Groups3 citations · 2022
- 8