Savvas Sampaziotis
Papers
2
Total Citations
6
H-Index
2
About
Savvas Sampaziotis is a researcher focused on advancing robotic manipulation and perception, with key contributions in non-prehensile manipulation and real-time robot-environment interaction. His work addresses fundamental challenges in enabling robots to handle objects without traditional grasping, particularly in scenarios where object dynamics and friction are unknown. In his most cited paper (2022, 4 citations), Sampaziotis introduced a model-free robot control method for dragging objects on planar surfaces using top contact forces—a technique that expands robotic grasping capabilities in diverse applications, from manufacturing to service robotics. He further explores robot perception in his 2023 work (2 citations), developing a lightweight method to detect dynamic target occlusions caused by the robot’s own body, enhancing autonomous navigation and manipulation in cluttered environments. Though early in his career, Sampaziotis’s research demonstrates a practical, computationally efficient approach to complex manipulation tasks, with potential for integration into real-world robotic systems. His work is particularly relevant for researchers in non-prehensile manipulation, robot control, and occlusion-aware perception, offering scalable solutions for robots operating under uncertainty.
Research Focus
Key Achievements
Top Papers
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- 2