Papers
5
Total Citations
41
H-Index
4
About
Mridul Aanjaneya is a leading researcher in robotics and differentiable physics, with a focus on tensegrity and cable-driven robots. His major contributions center on developing data-efficient differentiable physics engines that bridge the sim2real gap, enabling more accurate modeling and control of complex, deformable robots. Aanjaneya’s work has demonstrated how these engines can be trained with low-frequency data, drastically reducing the human effort required for policy learning in simulation. His most-cited papers, including "Sim2Sim Evaluation of a Novel Data-Efficient Differentiable Physics Engine for Tensegrity Robots" and "Real2Sim2Real Transfer for Control of Cable-Driven Robots Via a Differentiable Physics Engine," each with 15 citations, highlight his impact on advancing robot control in unstructured terrains. He has also extended his methods to low-cost wheeled mobile robots, showing the versatility of differentiable physics for model identification and control. Aanjaneya’s research is pivotal for soft and tensegrity robotics, offering scalable solutions for high-dimensional, hard-to-control systems, and his work continues to shape the future of simulation-to-reality transfer in robotics.
Research Focus
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