Prasad Kulkarni
Papers
1
Total Citations
14
H-Index
1
About
Prasad Kulkarni is a robotics researcher whose work centers on bipedal locomotion, path planning, and the integration of machine learning with robotic control systems. His most-cited paper, "Path Planning for a Statically Stable Biped Robot Using PRM and Reinforcement Learning" (2006), with 14 citations, represents a foundational contribution to the field of legged robotics. In this work, Kulkarni pioneered a novel approach that combines Probabilistic Roadmap (PRM) methods with reinforcement learning to enable a statically stable biped robot to navigate complex environments. This hybrid strategy addressed critical challenges in autonomous navigation, allowing the robot to efficiently plan collision-free paths while adapting its gait through learned policies. Kulkarni's research has implications for advancing humanoid robotics, assistive devices, and autonomous systems operating in unstructured terrains. His work is particularly notable for bridging classical motion planning with modern machine learning techniques, a direction that has since become central to robotics research. Though his citation count reflects a focused, early-career impact, Kulkarni's contributions remain relevant for students and researchers exploring the intersection of stability, learning, and path planning in legged systems.
Research Focus
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Top Papers
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