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About

Gaurang Sharma is a researcher advancing the frontiers of human-robot collaboration and robotic manipulation, with a focus on creating intuitive, multi-modal interaction systems for industrial applications. His work centers on sensor-based collaboration, co-speech gesture communication, and 6D pose estimation, aiming to make robots more responsive and dexterous partners in shared tasks. Sharma’s most-cited paper, “Sensor-based human–robot collaboration for industrial tasks” (2023, 39 citations), explores how interaction modalities can be tailored to industrial environments, addressing challenges like lighting and shared task coordination. He furthers this line of inquiry in “Co-Speech Gestures for Human-Robot Collaboration” (2023, 5 citations), proposing a multi-modal approach that integrates natural gestures with speech to enhance expressiveness and task assignment. In “6D Assembly Pose Estimation by Point Cloud Registration for Robotic Manipulation” (2024, 2 citations), Sharma tackles the critical need for precise perception in dexterous manipulation, using point cloud registration to bridge scene understanding and robot control. His work on multi-label annotation for visual multi-task learning models also supports scalable deep learning pipelines. With a growing citation impact and contributions that blend perception, interaction, and control, Sharma is shaping the next generation of collaborative and autonomous robotic systems.