Abhinav Gandhi
Papers
5
Total Citations
51
H-Index
4
About
Abhinav Gandhi is a robotics researcher whose work spans robot manipulation, visual servoing, and vision-based motion planning. His research is particularly focused on bridging perception and control, enabling robotic systems to operate intelligently using visual information alone. Gandhi's most-cited contribution, "Benchmarking Cluttered Robot Pick-and-Place Manipulation With the Box and Blocks Test" (2019, 32 citations), introduced a standardized benchmark for evaluating robotic manipulation capabilities, cleverly adapting a decades-old clinical rehabilitation assessment tool to the robotics domain — a creative cross-disciplinary innovation. His subsequent work pushed the boundaries of visual servoing, developing novel methods that control robotic manipulators in configuration space rather than focusing solely on end-effector pose. This includes skeleton-based and keypoint-based approaches that leverage depth imaging and natural robot features, reducing reliance on explicit models. More recently, Gandhi has extended visual control to continuum robots through clothoid-based shape control and developed image-space motion planning frameworks that eliminate the need for proprioceptive sensing entirely. Collectively accumulating over 50 citations, his body of work reflects a consistent vision: making robot control more flexible, model-free, and visually grounded — a research direction with significant implications for real-world robot deployment.
Research Focus
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Top Papers
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