Amir Zakerimanesh
Papers
7
Total Citations
187
H-Index
6
About
Amir Zakerimanesh is a researcher at the forefront of human-robot interaction and nonlinear teleoperation systems, with a primary focus on enhancing the safety, stability, and adaptability of robotic systems that physically collaborate with humans. His most impactful work, the 2021 survey "Impedance Variation and Learning Strategies in Human–Robot Interaction" (111 citations), provides a comprehensive framework for dynamically adjusting robotic impedance during physical human-robot collaboration, a critical capability for applications ranging from rehabilitation to industrial co-working. Zakerimanesh has made significant contributions to multilateral teleoperation, developing novel control strategies that address the dual challenges of time-varying communication delays and actuator saturation—practical constraints that often undermine system performance. His 2017 paper on dual-user nonlinear teleoperation (20 citations) and subsequent works on task-space synchronization (cumulatively over 40 citations) have advanced the theoretical foundations for cooperative teleoperation systems where multiple operators control a single remote robot. More recently, his 2024 work on iterative learning for gravity compensation in impedance control targets the critical domain of robot-assisted arthroscopic surgery, addressing dynamic uncertainties that threaten precision and stability in orthopedic procedures.
Research Focus
Key Achievements
Top Papers
- 1Impedance Variation and Learning Strategies in Human–Robot Interaction111 citations · 2021
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- 7Iterative Learning for Gravity Compensation in Impedance Control5 citations · 2024