Sucheth Shenoy
Research Interests
- Robot Learning
- Optimal Control and Estimation
- Optimal Transport and Gradient Flows on Probability Spaces
- Safe and Robust Reinforcement Learning
- Distributionally Robust Optimization
Contact
sucheth@robot-learning.de
sucheth.shenoy@tu-darmstadt.de
+49-6151-16-20811
Room E303, Building S2|02, Hochschulstr. 10, 64289 Darmstadt
Sucheth Shenoy joined the Intelligent Autonomous Systems (IAS) Lab at TU Darmstadt on April 01, 2026, as a PhD student. His research focuses on reinforcement learning and control, with an emphasis on developing learning-based control methods with stability and robustness guarantees for real-world robotic systems.
He completed his M.Sc. in Mechatronics at the Hamburg University of Technology (TUHH), specializing in Robotics and Intelligent Systems. His master’s thesis was conducted at the DECODE group at EPFL and focused on reinforcement learning for optimal control with stability guarantees. He is a recipient of the NCCR Automation Research Fellowship 2025 and the Deutschlandstipendium 2023, 2024. Prior to his master’s, he worked for one year at Bosch India in the Engine Management Systems domain. He obtained his bachelor’s degree in Mechanical Engineering from RV College of Engineering, Bangalore.
Publications
Control Theory
- Furieri, L.; Shenoy, S.; Saccani, D.; Martin, A.; Ferrari-Trecate, G. (2025). MAD: A Magnitude And Direction Policy Parametrization for Stability Constrained Reinforcement Learning, 2025 IEEE 64th Conference on Decision and Control (CDC).
- Shenoy, S.; Sharan, B.; Werner, H. (2025). Temporal Autoencoder for Identification and Predictive Control of Nonlinear Dynamics based on Koopman Operator Theory, 2025 American Control Conference (ACC).
Computer Vision for Table Tennis
- Kulkarni, K.M.; Jamadagni, R.S.; Paul, J.A.; Shenoy, S. (2023). Table Tennis Stroke Detection and Recognition Using Ball Trajectory Data, arXiv cs.CV.
- Kulkarni, K.M.; Shenoy, S. (2021). Table Tennis Stroke Recognition Using Two-Dimensional Human Pose Estimation, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).