Sai Krishna Allani

Papers

Total Citations

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About

Sai Krishna Allani’s research lies at the intersection of human perception, robotic manipulation, and grasp planning. His work investigates how humans naturally coordinate gaze and hand movements during object interaction, and how these insights can be transferred to teach robots more intuitive and effective grasping behaviors. In his highly cited study “Evaluating human gaze patterns during grasping tasks” (2016, 3 citations), Allani demonstrated that gaze patterns differ significantly when participants use their own hands versus a robotic hand, revealing key attentional strategies that can inform robot control systems. His follow-up work, “Human-Planned Robotic Grasp Ranges: Capture and Validation” (2016, 2 citations), addresses critical bottlenecks in learning from human demonstration—namely, inefficient data capture and poor generalization across objects and grasps. By proposing methods to capture and validate human-planned grasp ranges, Allani contributes to making robotic learning more scalable and robust. His research is particularly valuable for students and engineers working in human-robot interaction, offering a principled approach to bridging human dexterity with autonomous manipulation.