Marcin Paluch
Papers
1
Total Citations
1
H-Index
1
About
Marcin Paluch is a researcher at the forefront of real-time robotic control systems, specializing in the intersection of field-programmable gate arrays (FPGAs), neural networks, and nonlinear model predictive control (NMPC). His work addresses a critical bottleneck in robotics: the latency and computational cost that often prevent advanced control algorithms from running in real-time on embedded platforms. Paluch’s major contribution lies in demonstrating that inexpensive FPGA-implemented Neural Controllers (NC), trained via supervised learning to mimic NMPC, can achieve high-performance control in dynamic environments. His most-cited paper (2025) showcases this approach on two challenging platforms—the classic CartPole balancing task and the F1TENTH autonomous race car—proving that hardware-accelerated neural control can match the precision of NMPC while drastically reducing computational overhead. Though early in its citation impact, this work signals a paradigm shift toward practical, low-latency control for resource-constrained robots. Paluch’s research is particularly notable for bridging theory and deployment, offering a scalable path for real-time autonomy in drones, autonomous vehicles, and agile robotics.
Research Focus
Key Achievements
Top Papers
- 1FPGA Hardware Neural Control of CartPole and F1TENTH Race Car1 citations · 2025