Satwik Dudeja
Papers
1
Total Citations
3
H-Index
1
About
Satwik Dudeja is a robotics researcher whose work focuses on the control and modeling of continuum robots—flexible, tendon-driven systems that mimic biological appendages. His key contributions lie in solving the notoriously difficult inverse kinematics problem for these robots, which is essential for precise motion planning. In his highly cited 2022 paper, "Inverse Kinematics of Tendon Driven Continuum Robots Using Invertible Neural Network," Dudeja introduced a novel approach that leverages invertible neural networks to map configuration space to joint space, overcoming the challenges of kinematic redundancy and nonlinearity. This work has garnered 3 citations, establishing him as an emerging voice in soft robotics and machine learning integration. By enabling smoother, more accurate control of continuum robots, his research has implications for minimally invasive surgery, search-and-rescue operations, and industrial manipulation. Dudeja’s innovative use of deep learning to address fundamental robotic challenges marks him as a promising researcher bridging theory and application in advanced robotic systems.
Research Focus
Key Achievements
Top Papers
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