Hamsa Datta Perur
Papers
2
Total Citations
26
H-Index
2
About
Hamsa Datta Perur is a robotics researcher whose work bridges autonomous navigation and intelligent manipulation. Her primary research areas include simultaneous localization and mapping (SLAM) for autonomous ground vehicles and reinforcement learning for robotic object placement. Her most-cited paper, "Comparative analysis of ROS based 2D and 3D SLAM algorithms for Autonomous Ground Vehicles" (2020, 24 citations), provides a critical evaluation of SLAM techniques essential for enabling vehicles to navigate unknown environments by estimating sensor motion and reconstructing spatial structures—a foundational contribution to autonomous driving. More recently, Perur has advanced robotic manipulation with "Compact Multi-Object Placement Using Adjacency-Aware Reinforcement Learning" (2024, 2 citations), tackling the complex challenge of precisely arranging irregularly shaped objects with sensitive surfaces. This work introduces a reinforcement learning framework that enables robots to grasp objects from the side and place them without damaging surfaces, addressing a practical bottleneck in industrial and service robotics. Her research demonstrates a clear trajectory from environmental perception to dexterous physical interaction, showcasing her ability to solve real-world robotic challenges. With growing citations and innovative approaches, Perur is establishing herself as a promising voice in intelligent robotics.
Research Focus
Key Achievements
Top Papers
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