Niloufar Khorsandi
Fraunhofer Institute for Production Systems and Design Technology
Papers
1
Total Citations
38
H-Index
1
About
Niloufar Khorsandi is a leading researcher in autonomous mobile robotics, with a focus on dynamic obstacle avoidance and deep reinforcement learning (DRL). Her most-cited work, "Arena-Bench: A Benchmarking Suite for Obstacle Avoidance Approaches in Highly Dynamic Environments" (2022, 38 citations), introduces a standardized benchmarking framework that addresses a critical gap in the field: the lack of reproducible evaluation for learning-based navigation systems. By designing a comprehensive suite of challenging scenarios, Khorsandi enables fair comparison of DRL approaches, which have shown superior performance in dynamic environments but are often developed in ad-hoc simulators. This contribution has been instrumental in advancing the reliability and transparency of autonomous navigation research. Her work underscores the importance of rigorous benchmarking in translating cutting-edge DRL algorithms into real-world robotic applications, making her a key figure in the push toward safer, more robust autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1