Nicholas Mohammad
Papers
4
Total Citations
46
H-Index
3
About
Nicholas Mohammad is a leading researcher in autonomous robot navigation, specializing in motion planning for highly constrained and uncertain environments. His work bridges the gap between theoretical safety guarantees and real-world robustness, with a focus on both ground and aerial robots. He is best known for his contributions to the Benchmark Autonomous Robot Navigation (BARN) Challenge at ICRA 2022, which established a rigorous standard for evaluating navigation systems in cluttered spaces. His highly cited paper on Model Predictive Path Integral (MPPI) methods introduced a fast, proactive, and uncertainty-aware planning framework for UAVs, earning 14 citations for its novel approach to handling tracking errors. More recently, Mohammad has advanced robust autonomy through GP-based motion planning and soft actor-critic (SAC) reinforcement learning, enabling robots to dynamically adapt safety constraints and recover from failures in unknown environments. With over 46 total citations across his key works, his research is shaping the next generation of agile, resilient robots capable of operating safely alongside humans in unpredictable settings.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4