Alex Ansari
Papers
6
Total Citations
84
H-Index
6
About
Alex Ansari’s research lies at the intersection of modular robotics, optimal control, and real-time autonomy, with a focus on enabling robots to adapt and act intelligently in uncertain, dynamic environments. His major contributions include pioneering gait generation methods for rapidly reconfigurable modular legged robots, allowing them to simultaneously locomote and manipulate objects—work that has garnered 19 citations. He also advanced real-time trajectory synthesis by integrating Sequential Action Control with Fisher information maximization, demonstrated on the Baxter robot (17 citations), and developed minimum sensitivity control techniques to plan robustly under parametric and hybrid uncertainty (14 citations). Ansari’s dynamical systems approach to obstacle navigation enabled a blind, series-elastic hexapod robot to autonomously climb curbs and steps (14 citations), while his model-based reactive control framework extended Sequential Action Control to high-dimensional robotic systems (12 citations). Notably, his work on optimal control-on-request for assistive balance control (8 citations) showcases real-time, on-demand assistance for human-robot interaction. Collectively, Ansari’s research emphasizes reliability, speed, and adaptability, making significant strides in bridging control theory and practical robotic deployment.
Research Focus
Key Achievements
Top Papers
- 1Generating gaits for simultaneous locomotion and manipulation19 citations · 2017
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- 5Model-Based Reactive Control for Hybrid and High-Dimensional Robotic Systems12 citations · 2016
- 6