Sromona Chatterjee
Papers
2
Total Citations
19
H-Index
2
About
Sromona Chatterjee is a roboticist whose research lies at the intersection of bio-inspired locomotion, motor learning, and reinforcement learning. Her work focuses on enabling complex, snake-like robots with screw-drive units to achieve versatile, goal-directed movement. Chatterjee’s major contributions include pioneering the use of motor primitives—modular building blocks inspired by biological motor control—to organize and chain behaviors for these unconventional robots. In her 2015 paper, she demonstrated how primitive chaining could produce adaptive locomotion, while her 2014 study applied the Policy Improvement with Path Integrals (PI²) algorithm to generate goal-directed gaits. Though her most-cited works have modest citation counts (10 and 9, respectively), they represent foundational steps in applying reinforcement learning to non-wheeled, serpentine platforms. This work has implications for search-and-rescue robots and exploration in confined spaces. Chatterjee’s research bridges computational motor control and real-world hardware, offering a blueprint for teaching complex robots to move with flexibility and purpose. Her achievements highlight the power of combining biological principles with machine learning to solve challenging locomotion problems.
Research Focus
Key Achievements
Top Papers
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