Manu Lahariya
Papers
1
Total Citations
3
H-Index
1
About
Manu Lahariya is a researcher at the forefront of soft robotics and physics-informed machine learning, with a focus on bridging the gap between complex physical models and real-time control. His work centers on developing computationally efficient simulation frameworks for soft robotic manipulation, particularly using dielectric elastomer actuators (DEAs)—a class of smart materials that deform in response to electric fields. Lahariya’s key contribution lies in learning physics-informed surrogate models that approximate high-fidelity finite element method (FEM) simulations, enabling accurate yet fast predictions for control tasks such as gentle grasping and dexterous manipulation. His 2022 paper on this topic has garnered early citations, reflecting growing interest in data-driven approaches for deformable systems. By tackling the prohibitive computational cost of traditional FEM models, Lahariya’s research opens new pathways for real-time, adaptive control in soft robotics—a field critical for safe human-robot interaction. His work exemplifies how integrating physical principles with machine learning can unlock practical applications in automation, prosthetics, and beyond.
Research Focus
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Top Papers
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