Virinchi Roy Surabhi
Papers
1
Total Citations
4
H-Index
1
About
Virinchi Roy Surabhi is a researcher at the intersection of control theory, robotics, and deep learning, with a primary focus on developing novel algorithms for locomotion and autonomous systems. His most notable contribution is the introduction of a deep forward-backward stochastic differential equation (FBSDE) framework for learning locomotion controllers, as detailed in his 2021 paper "Learning Locomotion Controllers for Walking Using Deep FBSDE." This work innovatively integrates state constraints—such as those ensuring stable walking patterns or energy efficiency—directly into the FBSDE formulation, offering a principled and flexible approach to control under uncertainty. While his citation count is still growing, the work represents a significant methodological advance, bridging stochastic optimal control with modern deep learning. Surabhi’s research is particularly relevant for students and engineers seeking to design robust, constraint-aware controllers for legged robots and other dynamical systems. His approach opens new avenues for combining rigorous mathematical control theory with data-driven techniques, promising more reliable and efficient autonomous behavior in complex environments.
Research Focus
Key Achievements
Top Papers
- 1Learning Locomotion Controllers for Walking Using Deep FBSDE4 citations · 2021