Khaled Nakhleh
Papers
1
Total Citations
6
H-Index
1
About
Khaled Nakhleh is a robotics researcher whose work focuses on bridging the gap between reinforcement learning and real-world autonomous navigation. His primary research areas include collision avoidance, local path planning, and the application of deep RL algorithms to physical robotic systems. Nakhleh’s most notable contribution is the development of SACPlanner, a novel local planner that integrates the Soft Actor Critic algorithm with polar state representations to achieve robust, real-time collision avoidance. In his landmark 2023 paper, he demonstrated that recent enhancements to SAC—such as RAD and DrQ—enable near-perfect training performance after only 10,000 episodes, a significant leap in sample efficiency for embodied agents. This work has already garnered 6 citations, establishing Nakhleh as an emerging voice in the field of learning-based control for mobile robots. By validating his approach on physical platforms rather than in simulation alone, he has provided a practical blueprint for deploying RL-based planners in real-world environments. For students and researchers interested in the intersection of robot learning and safety-critical navigation, Nakhleh’s work offers a compelling case study in how algorithmic innovations can translate directly into deployable robotic systems.
Research Focus
Key Achievements
Top Papers
- 1