Ehsan Shahri
Papers
2
Total Citations
21
H-Index
2
About
Ehsan Shahri is a robotics researcher whose work focuses on bipedal locomotion, humanoid stability, and push recovery — critical challenges in enabling humanoid robots to operate reliably in dynamic, real-world environments. His most cited paper, “A reliable model-based walking engine with push recovery capability” (2017, 17 citations), presents a robust walking engine that allows adult-size humanoid robots to maintain balance and recover from external disturbances, addressing the inherent instability of bipedal systems. Shahri’s research is particularly relevant to competitive robotics platforms like RoboCup, where robots must withstand physical collisions during gameplay. In his related work, “How to Select a Suitable Action against Strong Pushes in Adult-Size Humanoid Robot: Learning from Past Experiences” (2016, 4 citations), he introduces a learning-based approach that enables robots to choose appropriate recovery actions based on past interactions, advancing adaptive control in humanoid robotics. Though his citation counts are modest, Shahri’s contributions are notable for their practical focus on real-time stability and collision resilience — essential capabilities for deploying humanoid robots in human-centered environments. His work bridges model-based control and experiential learning, offering valuable insights for researchers developing robust, autonomous humanoid systems.
Research Focus
Key Achievements
Top Papers
- 1A reliable model-based walking engine with push recovery capability17 citations · 2017
- 2