Bence Magyar

Heriot-Watt University, Heriot-Watt University Malaysia

Papers

Total Citations

H-Index

About

Bence Magyar is a robotics researcher whose work centers on motion planning, particularly for manipulation and mobile-base systems. His major contributions lie in advancing optimization-based approaches to handle the complex constraints of real-world robotic motion, such as joint limits, path smoothness, and mixed Cartesian and joint-space objectives. His most cited paper, "Timed-Elastic Bands for Manipulation Motion Planning" (2019, 28 citations), introduces a method that improves upon state-of-the-art techniques by enabling more flexible and efficient path generation under multiple conditions. Magyar also explores Learning from Demonstration (LfD) in "Guided Stochastic Optimization for Motion Planning" (2019, 12 citations), aiming to make robot teaching more scalable and intuitive for collaborative tasks. Additionally, his work on "Timed-Elastic Smooth Curve Optimization for Mobile-Base Motion Planning" (2019, 9 citations) extends these ideas to mobile robots, proposing a piecewise smooth curve planner that enhances trajectory quality and control. Through these contributions, Magyar has helped bridge the gap between theoretical motion planning and practical robotic applications, making his research valuable for both students and engineers working on autonomous systems.