Seyed Mohammad Jafar Jalali

About

Seyed Mohammad Jafar Jalali is a leading researcher in autonomous navigation and intelligent robotics, with a focus on neuroevolution and deep learning. His work bridges evolutionary algorithms and neural networks to create robust, adaptive systems for real-world robot control. Jalali’s most cited paper, “Neuroevolution-based autonomous robot navigation: A comparative study” (2020, 48 citations), systematically evaluates how evolutionary strategies can optimize neural models for navigation tasks. He further advanced this field with “Autonomous Robot Navigation System Using the Evolutionary Multi-Verse optimizer Algorithm” (2019, 27 citations), introducing a novel metaheuristic for training neural controllers. His research on “Autonomous Navigation via Deep Imitation and Transfer Learning” (2020) explores end-to-end learning for driving, addressing data scarcity challenges. Beyond navigation, Jalali has contributed to teleoperation systems, studying robust collaboration under random communication delays. With over 100 total citations, his work is widely recognized for improving the efficiency and reliability of autonomous agents. His comparative studies and algorithm innovations provide foundational tools for students and engineers developing next-generation robotics and AI systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Neuroevolution-based autonomous robot navigation: A comparative study

   48 citations · 2020
2. 2
   Autonomous Robot Navigation System Using the Evolutionary Multi-Verse optimizer Algorithm

   27 citations · 2019
3. 3
   Autonomous Robot Navigation Using Moth-Flame-Based Neuroevolution

   19 citations · 2019
4. 4
   Autonomous Navigation via Deep Imitation and Transfer Learning: A Comparative Study

   6 citations · 2020
5. 5
   Robust Collaboration of a Haptically-Enabled Double-Slave Teleoperation System under Random Communication Delays

   4 citations · 2020

Key Collaborators

Contact & Links