Mohammad Ansari
Papers
1
Total Citations
2
H-Index
1
About
Mohammad Ansari is a robotics researcher whose work centers on humanoid robot control, motion imitation, and balance stability. His most-cited paper, "Balance Strategy for Human Imitation by a NAO Humanoid Robot" (2017), introduces an ankle-based balance approach that leverages an inverted pendulum model and computed Center of Mass (CoM) to maintain stability during dynamic human motion replication. This contribution addresses a fundamental challenge in humanoid robotics: enabling robots to mimic complex human movements without falling. By calculating the support polygon for both double and single support phases, Ansari’s strategy provides a practical framework for real-time balance correction. Though his citation count is modest (2 citations), his work is notable for its direct application to the widely used NAO platform, making it accessible for further research in human-robot interaction and assistive robotics. Ansari’s focus on bridging human motion and robotic execution highlights his commitment to advancing intuitive, stable humanoid systems—a critical step toward robots that can safely operate alongside people in everyday environments.
Research Focus
Key Achievements
Top Papers
- 1Balance Strategy for Human Imitation by a NAO Humanoid Robot2 citations · 2017