Mohammad Mohtashami
Papers
1
Total Citations
2
H-Index
1
About
Mohammad Mohtashami is an emerging researcher in the fields of robotics and reinforcement learning, with a particular focus on control systems for rehabilitation applications. His most-cited work, "PPO and SAC Reinforcement Learning Based Reference Compensation" (2024), introduces a novel approach to enhancing robotic precision by using Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) algorithms. This method compensates for dynamic disturbances in the UR5e robot, enabling it to accurately reach fixed points and perform smooth, precise tracking of predefined movements—critical for tasks like physical therapy and assistive robotics. With 2 citations to date, this paper signals growing interest in his work within the robotics community. Mohtashami’s contributions lie at the intersection of machine learning and mechanical control, offering practical solutions for real-world robotic systems. His research holds promise for advancing autonomous rehabilitation technologies, where safe, adaptive, and accurate robot motion is essential. As his citation count grows, Mohtashami is establishing himself as a thoughtful innovator in reinforcement learning-driven robotic control.
Research Focus
Key Achievements
Top Papers
- 1PPO and SAC Reinforcement Learning Based Reference Compensation2 citations · 2024