Papers
8
Total Citations
47
H-Index
5
About
Aravind Sivaramakrishnan is a robotics researcher whose work sits at the intersection of motion planning, machine learning, and dynamical systems analysis. His research primarily focuses on advancing sampling-based motion planning algorithms, particularly kinodynamic planning for vehicular and robotic systems operating in complex, real-world environments. Sivaramakrishnan's most influential contributions center on integrating machine learning with classical planning frameworks. His widely cited survey on machine learning and sampling-based motion planning (2022, 14 citations) provides a comprehensive synthesis of this rapidly evolving field, serving as a key reference for researchers navigating this interdisciplinary space. His practical work, including "Improving Kinodynamic Planners for Vehicular Navigation with Learned Goal-Reaching Controllers" and terrain-aware learned controllers, demonstrates how reinforcement learning can significantly enhance planning efficiency and path quality in challenging scenarios. Notably, his research also ventures into topological analysis of robot controllers through Morse graphs, offering principled tools for understanding global dynamical behavior with strong theoretical guarantees. His earlier work on household object recognition using SIFT and SVMs reflects a broader computer vision foundation underpinning his robotics expertise. Collectively accumulating over 47 citations, Sivaramakrishnan's work meaningfully bridges theoretical rigor with practical applicability in autonomous robot navigation.
Research Focus
Key Achievements
Top Papers
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- 6Towards Learning Efficient Maneuver Sets for Kinodynamic Motion Planning4 citations · 2019
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