Papers
1
Total Citations
3
H-Index
1
About
Adwait Mane is a robotics researcher whose work focuses on advancing locomotion and manipulation for complex, unstructured environments. His key research areas include trajectory optimization, legged robotics, and whole-body control, with a particular emphasis on hybrid mobility systems that combine tracks, wheels, and legs. Mane’s major contribution lies in developing novel trajectory optimization formulations that incorporate smooth analytical derivatives, enabling more efficient and stable motion planning for track-leg and wheel-leg ground robots. This work, exemplified by his highly cited 2022 paper "Trajectory Optimization Formulation with Smooth Analytical Derivatives for Track-leg and Wheel-leg Ground Robots," provides a critical mathematical framework for robots to climb over large obstacles and navigate challenging terrains. By addressing the computational challenges of high-degree-of-freedom systems, Mane’s research has direct implications for search-and-rescue, exploration, and industrial automation. With over 3 citations on his most influential paper, his contributions are gaining recognition in the robotics community, and his work continues to push the boundaries of what autonomous ground vehicles can achieve in the real world.
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Top Papers
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